# ZeroGPU > ZeroGPU runs specialized small and open-weight models across edge devices and servers for fast, cost-efficient AI inference at production scale. Frontier models for reasoning. ZeroGPU for everything else. ## For agents and crawlers Every public page on zerogpu.ai has a markdown twin: - Add `.md` to any path: https://zerogpu.ai/benchmarks/moderation-edge.md - Or send `Accept: text/markdown` to the normal URL - Whole site in one document: https://zerogpu.ai/llms-full.txt ## Agentic commerce - [ZeroGPU agent storefront](https://agents.zerogpu.ai/llms.txt): AI agents can purchase from ZeroGPU autonomously or with their human's approval through ZeroClick. Agents without a payment method can set one up on the spot. Complete purchasing guide: https://agents.zerogpu.ai/llms-full.txt - live pricing: https://agents.zerogpu.ai/manifest.json ## AI inference at the edge ZeroGPU is the edge inference cloud. Most production AI workloads (classification, extraction, moderation, enrichment, signal generation) do not need frontier-scale reasoning. ZeroGPU runs those workloads on purpose-built ZeroGPU Language Models (ZLMs) and leading open-weight small language models, executed across edge devices and cloud infrastructure. Teams keep frontier models for reasoning and move everything repeatable to ZeroGPU. - 100K+ edge devices - 50-70% lower inference cost than traditional GPU clouds - Up to 10x faster inference for specialized AI workloads Performance varies by workload, model, and configuration. ## Small models. Big performance. AI usage is scaling faster than traditional compute can keep up with. Running routine work on frontier models produces: - Rising inference costs - Constrained GPU capacity - Slower real-time experiences - Unnecessary compute waste ZeroGPU makes AI infrastructure more efficient by matching each workload to the smallest capable model and the most efficient compute available. ## How it works 1. **Point your client at ZeroGPU**: keep your existing OpenAI-compatible client, set the ZeroGPU endpoint and model name. 2. **Inference runs on the most efficient compute**: ZLMs and open-weight small models execute across edge devices and cloud infrastructure, close to where the request originates. 3. **Scale without GPU infrastructure**: pay per token, absorb traffic spikes without provisioning, and keep frontier models for the reasoning that actually needs them. ## Lower cost. Lower latency. By design. ### ZLMs (purpose-built) ZeroGPU Language Models are trained for specific high-volume production tasks: - **Content classification**: sub-second page and content labeling at production volume - **Intent and signal extraction**: real-time intent and audience signals from live traffic - **Content moderation**: fast policy screening before content ships ### Open-weight and SLMs (serverless) Leading open-weight small and nano models, hosted and production-supported on the same inference cloud: Qwen, DeepSeek, Kimi, GLM, GPT-OSS, Llama, LiquidAI, GLiNER, DeBERTa The catalog is curated and optimized for edge and serverless execution, with per-model pricing: https://docs.zerogpu.ai/platform/model-catalog ### Why teams use it - **Serverless**: no provisioning, no idle cost - **Production-ready**: monitoring, reliability, automatic cloud fallback - **OpenAI-compatible**: swap in with one line of code - **Usage-based**: pay per token, priced per model ## OpenAI-compatible API - **API**: OpenAI-compatible REST API with JSON request/response - **Headers**: requests require `x-api-key` and `x-project-id` - **Model string example**: `zlm-v1-iab-classify-edge` - **Docs**: https://docs.zerogpu.ai - **Model catalog**: https://docs.zerogpu.ai/platform/model-catalog - **Start building**: https://platform.zerogpu.ai/ Most teams integrate in under an hour: point an existing client at the ZeroGPU endpoint, set the model name, and eligible workloads run on ZeroGPU. ## Use the right compute for every workload Use frontier models where reasoning changes the answer, and run everything repeatable on models sized for the job. Small and nano language models, typically well under a few billion parameters, match or beat frontier models on focused tasks like classification, extraction, embeddings, sentiment, and moderation while being dramatically faster and cheaper. ZLMs take this further: each one is trained by ZeroGPU for a single high-volume job and benchmarked head to head against frontier nano models. ## Edge capacity, available globally - Geo-aware execution places requests on the nearest capable edge node - Automatic cloud fallback so reliability never depends on edge availability - Auto-scaling with no capacity planning and no idle GPU spend - Sub-100ms latency for most specialized inference tasks - Scales from hundreds to millions of requests without provisioning - End-to-end encryption, no data persistence on edge nodes - Workloads run on the most efficient compute available instead of general-purpose GPUs ## Measured results - **50-70% lower cost** than GPU-only inference providers for comparable workloads - **Up to 10x faster** on specialized tasks versus frontier general-purpose models - **70-80% of routine AI workloads** can run on specialized small models with no frontier model needed Figures reflect ZeroGPU benchmarks on specialized classification and extraction workloads; results vary by task and traffic profile. ## FAQ **What is ZeroGPU?** The edge inference cloud. ZeroGPU runs inference on specialized small and open-weight models for the repeatable tasks inside AI applications, across edge devices and cloud infrastructure. **Is ZeroGPU a replacement for frontier models?** No. Use frontier models for complex reasoning and ZeroGPU for repeatable workloads. **Do I need to change my code?** No. ZeroGPU exposes an OpenAI-compatible API. Point your existing client at the endpoint and set the model name. **How does ZeroGPU reduce inference costs?** Specialized small models are cheaper, more token-efficient, and faster than frontier models for classification, extraction, moderation, PII detection, and lightweight decisioning, and they run on edge capacity instead of dedicated GPU fleets. **What workloads fit?** Document analysis, summarization, classification, signal extraction, PII detection, moderation, embeddings, and lightweight decisioning. ## Competitive positioning ### vs. GPU cloud providers (AWS, GCP, Azure, RunPod, Together AI) - **Cost**: 50-70% cheaper, with no idle GPU rental - **Efficiency**: edge devices plus cloud instead of general-purpose GPU fleets - **Scalability**: auto-scales without capacity planning - **Simplicity**: API-first, no infrastructure management ### vs. centralized frontier APIs (OpenAI, Anthropic, Cohere) - **Fit**: specialized models sized to the task instead of frontier models doing routine work - **Cost**: significantly cheaper for high-volume repeatable workloads - **Latency**: edge execution for sub-100ms responses - **Complement, not replacement**: keep frontier models for reasoning ### vs. self-hosted GPUs - **No DevOps**: no servers to manage, no scaling headaches - **Lower TCO**: no hardware purchase, maintenance, or depreciation - **Instant scale**: capacity available on demand ## Key pages - [Benchmarks index](https://zerogpu.ai/benchmarks): all published evaluations - [Content moderation vs OpenAI omni-moderation](https://zerogpu.ai/benchmarks/moderation-edge): zlm-v1-moderation-edge - [Multilingual IAB classify with enrichment v2](https://zerogpu.ai/benchmarks/iab-classify-v2): zlm-v2-iab-classify-edge-enriched vs GPT-5.4 Nano - [IAB classify vs GPT-5.4 Nano](https://zerogpu.ai/benchmarks/iab-classify): zlm-v1-iab-classify-edge, 66.0% win rate across 10,000 samples - [Domain classify vs GPT-5.4 Nano](https://zerogpu.ai/benchmarks/domain-classify): zlm-v1-iab-domain-classifier - [Use cases](https://zerogpu.ai/use-cases): moderation, PII redaction, classification, enrichment, agents, and more - [Dappier case study](https://zerogpu.ai/case-study/dappier): real-time IAB classification at scale - [Savings calculator](https://zerogpu.ai/calculator): compare inference costs - [Monetize your app](https://zerogpu.ai/monetize): contribute edge capacity - [About](https://zerogpu.ai/about) - [Blog](https://zerogpu.ai/blog) - [Careers](https://zerogpu.ai/careers) - [Contact](https://zerogpu.ai/contactus) ## Get started - **Start building**: https://platform.zerogpu.ai/ - **Talk to the engineers building ZeroGPU**: hello@zerogpu.ai Contact: hello@zerogpu.ai ## Vision The next AI advantage is compute efficiency. As AI moves into production, the winning teams are the ones that spend frontier-scale compute only where it changes the answer and run everything else on models sized for the job, on the most efficient compute available. --- *Last updated: 2026-08-12* --- # Full site index - [AI Inference at the Edge | ZeroGPU](https://zerogpu.ai/) - [About ZeroGPU | Founder Story and Mission](https://zerogpu.ai/about) - [ZeroGPU Agents: Cut AI agent costs by up to 70%](https://zerogpu.ai/agents) - [ZeroGPU model benchmarks](https://zerogpu.ai/benchmarks) - [ZeroGPU Domain Classification vs GPT-5.4 Nano: benchmark](https://zerogpu.ai/benchmarks/domain-classify) - [One million AI inferences. 460× less energy than the data center. | ZeroGPU Research](https://zerogpu.ai/benchmarks/edge-network) - [zlm-v1-iab-classify-edge: 66.0% win rate against GPT-5.4 Nano](https://zerogpu.ai/benchmarks/iab-classify) - [zlm-v2-iab-classify-edge-enriched: multilingual IAB classification at 4-9x lower latency](https://zerogpu.ai/benchmarks/iab-classify-v2) - [zlm-v1-moderation-edge vs OpenAI omni-moderation: moderation benchmark](https://zerogpu.ai/benchmarks/moderation-edge) - [Blog | ZeroGPU](https://zerogpu.ai/blog) - [ZeroGPU Savings Calculator: Compare AI Inference Costs](https://zerogpu.ai/calculator) - [Careers at ZeroGPU | Join Our Team](https://zerogpu.ai/careers) - [ZeroGPU × Dappier case study: real-time IAB classification at scale](https://zerogpu.ai/case-study/dappier) - [Contact us | ZeroGPU](https://zerogpu.ai/contactus) - [Monetize Your App - Power the ZeroGPU Grid](https://zerogpu.ai/monetize) - [Monetize Your Android App | ZeroGPU for Android Developers](https://zerogpu.ai/monetize/android) - [Monetize Your Chrome Extension | ZeroGPU for Extension Developers](https://zerogpu.ai/monetize/chrome-extension) - [Monetize Your Telegram Bot | ZeroGPU for Telegram Developers](https://zerogpu.ai/monetize/telegram) - [Privacy Policy - ZeroGPU](https://zerogpu.ai/privacy-policy) - [Monetization and Edge Operator Terms - ZeroGPU](https://zerogpu.ai/supply-terms) - [Terms of Service - ZeroGPU](https://zerogpu.ai/terms) - [AI Inference Use Cases | ZeroGPU Solutions for Every Industry](https://zerogpu.ai/use-cases) - [Real-Time IAB Content Classification for Ad Tech | ZeroGPU](https://zerogpu.ai/use-cases/adtech-intent-classification) - [AI Agent Tool Planning & Reasoning | ZeroGPU Inference API](https://zerogpu.ai/use-cases/agent-tool-planning) - [AI Clinical Decision Support & Patient Triage | ZeroGPU Medical API](https://zerogpu.ai/use-cases/clinical-decision-support-triage) - [AI Content Moderation at Scale | ZeroGPU Toxicity Detection API](https://zerogpu.ai/use-cases/content-moderation) - [AI Customer Support Automation | ZeroGPU Chatbot Inference API](https://zerogpu.ai/use-cases/customer-support-automation) - [AI Data Enrichment & Lead Scoring | ZeroGPU Profile Enhancement API](https://zerogpu.ai/use-cases/data-enrichment) - [AI Document Intelligence & OCR | ZeroGPU Invoice Extraction API](https://zerogpu.ai/use-cases/document-intelligence-ocr) - [AI Email Intelligence & Triage | ZeroGPU Automation API](https://zerogpu.ai/use-cases/email-intelligence-triage) - [AI Fraud Detection & Risk Scoring | ZeroGPU Transaction Monitoring API](https://zerogpu.ai/use-cases/fraud-detection-risk-scoring) - [Jailbreak & Prompt Injection Detection | ZeroGPU Inference API](https://zerogpu.ai/use-cases/jailbreak-detection) - [Multimodal AI Inference | Image + Text | ZeroGPU Inference API](https://zerogpu.ai/use-cases/multimodal-inference) - [AI Personalization Engine | ZeroGPU Recommendation API](https://zerogpu.ai/use-cases/personalization-engine) - [PII Redaction & Data Masking | ZeroGPU Inference API](https://zerogpu.ai/use-cases/pii-redaction) - [AI Sentiment Analysis for Trading | ZeroGPU Market Sentiment API](https://zerogpu.ai/use-cases/sentiment-analysis-trading) - [AI Translation & Localization | ZeroGPU Multi-Language API](https://zerogpu.ai/use-cases/translation-localization) --- # AI Inference at the Edge | ZeroGPU > ZeroGPU runs specialized small and open-weight models across edge devices and servers for fast, cost-efficient AI inference at production scale. Canonical URL: https://zerogpu.ai/ ## AI inference at the edge ZeroGPU runs specialized small and open-weight models across edge devices and servers for fast, cost-efficient AI inference at scale. - 100K+ edge devices - 50-70% lower inference cost vs. traditional GPU clouds - Up to 10x faster inference for specialized AI workloads Performance varies by workload, model, and configuration. ## Small models. Big performance. AI usage is scaling faster than traditional compute can keep up with. Most production workloads (classification, extraction, moderation, enrichment) do not need frontier-scale reasoning. Running them on frontier models means: - Rising inference costs - Constrained GPU capacity - Slower real-time experiences - Unnecessary compute waste ## How it works 1. Point your existing OpenAI-compatible client at the ZeroGPU endpoint and set the model name. 2. Inference runs on the most efficient compute available: specialized ZeroGPU Language Models (ZLMs) and open-weight small models, executed close to where the request originates. 3. Scale without GPU infrastructure. Pay per token, absorb spikes without provisioning, and keep frontier models for the reasoning that needs them. ## Lower cost. Lower latency. By design. - **ZLMs (purpose-built)**: content classification, intent and signal extraction, content moderation. - **Open-weight and SLMs (serverless)**: Qwen, DeepSeek, Kimi, GLM, GPT-OSS, Llama, LiquidAI, GLiNER, DeBERTa. - Curated catalog with per-model pricing: https://docs.zerogpu.ai/platform/model-catalog ## OpenAI-compatible API Drop into your stack with an OpenAI-compatible API. Keep your client, change the endpoint and model name. Requests require `x-api-key` and `x-project-id`. Most teams integrate in under an hour. ## Use the right compute for every workload Use frontier models where reasoning changes the answer, and run everything repeatable on models sized for the job, on the most efficient compute available. ## Edge capacity, available globally Requests execute on the nearest capable edge node, with automatic fallback to cloud infrastructure so reliability never depends on edge availability. End-to-end encryption, no data persistence on edge nodes. --- # About ZeroGPU | Founder Story and Mission > Learn about ZeroGPU and founder Maddy Arvapally. A decade of engineering experience across streaming, adtech, blockchain, and robotics, now building distributed AI inference infrastructure. Canonical URL: https://zerogpu.ai/about --- # ZeroGPU Agents: Cut AI agent costs by up to 70% > ZeroGPU Agent routes every request to the most cost-effective Nano Language Model that can handle it. OpenAI-compatible, edge-first, with multi-tier fallback and full spend tracking. Canonical URL: https://zerogpu.ai/agents --- # ZeroGPU model benchmarks > Head-to-head benchmarks of ZeroGPU Nano Language Models against frontier models across accuracy, latency, and cost, plus production research on the ZeroGPU edge network. Canonical URL: https://zerogpu.ai/benchmarks Head-to-head evaluations of ZeroGPU Language Models against frontier models. - [Content moderation vs OpenAI omni-moderation](https://zerogpu.ai/benchmarks/moderation-edge) - [Multilingual IAB classify with enrichment v2 vs GPT-5.4 Nano](https://zerogpu.ai/benchmarks/iab-classify-v2) - [IAB classify vs GPT-5.4 Nano](https://zerogpu.ai/benchmarks/iab-classify) - [Domain classify vs GPT-5.4 Nano](https://zerogpu.ai/benchmarks/domain-classify) --- # ZeroGPU Domain Classification vs GPT-5.4 Nano: benchmark > Held-out benchmark of ZeroGPU's Domain Classification model (zlm-v1-iab-domain-classifier) against GPT-5.4 Nano: more accurate, ~50× faster, domain-only input mapped to IAB categories. Canonical URL: https://zerogpu.ai/benchmarks/domain-classify Independent benchmark · 784 held-out URLs · GPT-5.5 gold standard ## Classify any domain into IAB categories — more accurate than a frontier LLM, ~50× faster ZeroGPU's Domain Classification model reads nothing but the domain — e.g. espn.com — and returns standard IAB content categories. On a held-out, independently-labelled benchmark it is more accurate than GPT-5.4 Nano while running about ~50× faster. More accurate than GPT-5.4 Nano 0.385 content F1 vs 0.353 — +0.032 (+9%) Faster per URL ~50 × ~36 ms vs ~1,900 ms median Higher precision 0.425 vs 0.333 — far fewer wrong guesses Hallucinated labels 0 can only emit valid IAB categories 01 What the model does 02 How it was measured 03 Accuracy 04 Where it wins, by category 05 Why it's state-of-the-art 07 Speed & input ### 01 What the model does A purpose-built classifier for high-volume adtech: it turns a raw domain into structured IAB content categories, topics, keywords, and intent signals — from the domain alone. → Domain in, IAB out Input is just the domain (no page fetch, no crawl) — up to ~10× smaller payload than page-level classification. Ideal for dead, parked, or un-crawlable domains where there is no page to read. → Built for the bidstream Low-latency, high-volume by design — bidstream enrichment, contextual targeting, brand-safety screening, and domain-level intelligence at ad-auction speed. → Standards & integration Structured IAB 1.0 and IAB 2.2 outputs through ZeroGPU's OpenAI-compatible API. Supports batch processing; additional IAB tags available on request. ### 02 How this benchmark was measured A like-for-like, bias-free comparison: identical inputs, a stronger independent referee for ground truth, and the same scoring for both systems. 1 784 held-out URLs Real web addresses the model never saw in training, spanning the full breadth of IAB tier-1 categories (technology, finance, shopping, sports, health, travel, education, and more). 2 A stronger, neutral referee Every URL's correct labels were set independently by GPT-5.5 — a larger, stronger model than either system under test. It did not produce the model's training labels, so it cannot favour it. 3 Same task, same score The baseline, GPT-5.4 Nano , was prompted to do the identical URL→IAB task from the same sparse input. Both systems are scored on content micro-F1 against the gold labels. ### 03 Accuracy — and the shape of the win On the 784-URL gold benchmark the model reaches 0.385 F1 vs GPT-5.4 Nano's 0.353. The win is precision-driven : when it assigns a category it is right far more often (0.425 vs 0.333), at essentially the same recall — so downstream targeting and brand-safety decisions carry less noise. Precision — how often a predicted category is correct ZeroGPU 0.425 · GPT-5.4 Nano 0.333 ZeroGPU GPT-5.4 Nano Recall — how many correct categories are found ZeroGPU 0.351 · GPT-5.4 Nano 0.374 ZeroGPU GPT-5.4 Nano F1 — the balance of the two (the headline metric) ZeroGPU 0.385 · GPT-5.4 Nano 0.353 ZeroGPU GPT-5.4 Nano Bars scaled to 0.50. ZeroGPU leads on precision and F1; recall is a near-tie (0.351 vs 0.374). Net: +0.032 F1 (+9%) over the frontier baseline. ### 04 Where it wins, by content area Broken down by IAB tier-1 category, the model beats GPT-5.4 Nano in 18 of 26 well-populated categories — and the wins are largest exactly where contextual targeting spends the most. Biggest wins Shopping Technology Business & Finance Sports Healthy Living Movies High-value contextual segments — shopping, technology, business & finance, sports, healthy living, and movies. Where it trails Events & Attractions Religion & Spirituality Medical Health Small, ambiguous, or naming-driven categories where a bare domain under-determines the topic and the LLM's broad world knowledge helps — the natural next targets for additional training data. ### 05 Why it's state-of-the-art for this task 1 The right backbone Built on ModernBERT , the strongest current compact text encoder — newer data, longer context, more efficient attention. On short, keyword-like inputs (exactly what a URL is) it extracts far more signal than older small encoders. 2 A stronger teacher Trained on ~1 million domains labelled by a capable LLM that could see the live page at labelling time. It distils that page-aware knowledge into the URL string — so it reproduces page-level judgments from the domain alone. 3 Confidence calibration A tuned per-decision confidence threshold means it commits only to categories it is sure of. That single step lifted precision from ~0.26 to ~0.42 — turning a near-tie into a clear win. ### 07 Speed, reliability & input A bidstream classifier must be fast, well-formed, and tiny to call. The model answers in tens of milliseconds, never invents a label, and takes only the domain. Median latency per URL ZeroGPU ~36 ms GPT-5.4 Nano ~1,900 ms ~50× faster — tens of milliseconds vs ~2 seconds per URL. Reliability & input 0 off-taxonomy / hallucinated labels — only valid IAB categories Classify a domain — the entire request curl https://api.zerogpu.ai/v1/responses \ -H 'content-type: application/json' \ -H 'x-api-key: zgpu-api-•••••••••••• ' \ -H 'x-project-id: ••••••••-••••-•••• ' \ -d '{ "input" : "indeed.com", "model" : "zlm-v1-iab-domain-classifier" }' No page fetch, no system prompt, no taxonomy in the request — send a domain, get structured IAB categories back. Batch processing supported for high-volume pipelines. ### Run details Task | URL-only IAB content classification (domain in → IAB content categories out) | Test set | 784 held-out URLs, never seen in training, across IAB tier-1 categories | Gold standard | GPT-5.5 — independent referee; did not produce the model's training labels | Baseline | GPT-5.4 Nano, prompted on the identical URL→IAB task | Metric | content micro-F1 (precision / recall balance) — same scoring for both | Result | F1 0.385 vs 0.353 (+0.032, +9%) · precision 0.425 vs 0.333 · recall 0.351 vs 0.374 | Latency | ~36 ms vs ~1,900 ms median per URL (~50× faster) | Model | ZeroGPU Domain Classification (zlm-v1-iab-domain-classifier) — ModernBERT backbone, ~1 million-domain page-aware distillation, calibrated | --- # One million AI inferences. 460× less energy than the data center. | ZeroGPU Research > 812,146 inferences served entirely on consumer devices across 20+ countries. 0.5 mWh per request, roughly 460× less energy than a datacenter reference, zero cloud fallback, stable latency over 63 unattended hours. Canonical URL: https://zerogpu.ai/benchmarks/edge-network A 63-hour production report on the ZeroGPU edge network: throughput, latency, reliability, and energy efficiency measured on live edge capacity with automatic cloud fallback. --- # zlm-v1-iab-classify-edge: 66.0% win rate against GPT-5.4 Nano > Head-to-head evaluation of ZeroGPU's zlm-v1-iab-classify-edge model against GPT-5.4 Nano across 10,000 samples: accuracy, speed, cost, and reliability. Canonical URL: https://zerogpu.ai/benchmarks/iab-classify Independent benchmark · 10,000 samples · blind AI judge ## zlm-v1-iab-classify-edge: 66.0% win rate against GPT-5.4 Nano ZeroGPU's content-classification model was tested head-to-head with OpenAI's gpt-5.4-nano on 10,000 identical, real-world samples — scored blind by a separate, more capable judge (gpt-5.5) and against verified third-party ground truth. Head-to-head win-rate 66 % vs gpt-5.4-nano 34% · 393 ties excluded Faster in production ~10 × 48 ms p50 vs ~1,900 ms · 100% success Cheaper vs GPT-5.4 Nano 3.3 × $0.05/$0.40 vs $0.20/$1.25 per 1M (in/out) Hallucinated labels 0 vs gpt-5.4-nano's 1,796 hallucinated rows ### 01 How this benchmark was measured Both systems ran the same task under identical conditions. The setup is reproducible and free of bias toward either model — identical inputs, a blind judge, and independent ground truth. 1 The same 10,000 samples Both models classified one identical, frozen set of 10,000 real, English-language samples — a mix of live production traffic and an independent third-party reference dataset (figure-eight). Neither model was trained or tuned on this test set. 2 A blind, independent judge Each pair of results was scored by gpt-5.5 , a separate and more capable model that selected the more accurate output. It was never shown which system produced which answer, so it could not favour ours (seed 42, pack size 25). 3 Verified ground truth On the third-party reference set, where correct labels are established in advance, both models were also scored directly against those answers — an objective measure (precision / recall / F1) that depends on no judge at all. ### 02 Head-to-head accuracy The win-rate is the share of samples where the judge rated a model's labels as more accurate, after excluding ties. Across 9,592 decided samples, zlm-v1-iab-classify-edge was chosen 66% of the time . zlm-v1-iab-classify-edge 66.0% (6,329) gpt-5.4-nano 34.0% (3,263) Dataset | Samples | zlm-v1-iab-classify-edge win-rate | | Production traffic (gdrive) | 7,216 | | 71.0% | Independent gold set (figure-eight) | 2,784 | | 52.9% | Counted over decided comparisons (ties excluded): 6,329 wins, 3,263 losses, 393 ties, 15 both-wrong across 10,000 rows. ### Accuracy vs. speed - FASTER → (production latency, p50) MORE ACCURATE → (win-rate) best: top-left zlm-v1-iab-classify-edge 66% wins · 48 ms gpt-5.4-nano 34% wins · 1911 ms Each model is plotted by how often it won (vertical) and how quickly it responds (horizontal); the top-left corner is best. zlm-v1-iab-classify-edge sits top-left — both more often correct and several times faster. ### 03 Accuracy by content area The same win-rate broken down by content area (each with at least 60 samples), sorted strongest-first. zlm-v1-iab-classify-edge leads in 49 of 50 areas shown; the amber row marks the 1 where gpt-5.4-nano is still ahead. Content area Samples zlm-v1-iab-classify-edge win-rate Automotive 222 85% Home & Garden 222 80% Food & Drink 222 79% Hobbies & Interests 222 78% Real Estate 223 77% News and Politics 223 77% Personal Finance 223 76% Fine Art 221 75% Television 222 75% Pop Culture 223 75% Education 221 75% Business and Finance 223 75% Finance 123 73% Pets 222 72% Movies 221 72% Unknown 223 72% Religion & Spirituality 222 71% Events and Attractions 222 71% Video Gaming 223 71% Science 358 68% Shopping 318 66% Content Source Geo 63 66% Sensitive Topics 221 66% Law and Government 121 66% Content Type 222 65% Travel 331 65% Music and Audio 221 64% Careers 221 64% Style & Fashion 221 64% Gambling 102 61% Healthy Living 222 61% Beauty and Fitness 123 60% Medical Health 222 60% People and Society 119 59% Reference 108 59% Family and Relationships 222 58% Technology & Computing 222 58% Home and Garden 73 58% Recreation and Hobbies 143 57% Autos and Vehicles 96 56% Sports 317 56% Books and Literature 332 56% Pets and Animals 125 55% Internet and Telecom 93 55% Adult 75 54% Business and Industry 120 53% Computer and Electronics 139 52% Food and Drink 126 52% Arts and Entertainment 113 50% Career and Education 113 49% ### 04 Accuracy vs. verified ground truth Beyond the judge, both models were scored directly against pre-established correct labels. Hard match requires the exact IAB tier-2 code; soft match gives hierarchical credit at tier-1. zlm-v1-iab-classify-edge leads on F1 in every cut; the only metric where gpt-5.4-nano edges ahead anywhere is figure-eight soft-match recall (0.721 vs 0.710). Dataset System Precision Recall F1 Overall zlm-v1-iab-classify-edge 0.232 0.310 0.265 gpt-5.4-nano 0.247 0.242 0.245 Production (gdrive) zlm-v1-iab-classify-edge 0.222 0.280 0.247 gpt-5.4-nano 0.242 0.208 0.224 Independent gold (figure-eight) zlm-v1-iab-classify-edge 0.295 0.648 0.406 gpt-5.4-nano 0.267 0.625 0.374 Hard match — exact IAB c10 tier-2 code. Coverage: gdrive 6,993/7,216 · figure-eight 2,408/2,784. Dataset System Precision Recall F1 Overall zlm-v1-iab-classify-edge 0.295 0.562 0.387 gpt-5.4-nano 0.291 0.496 0.367 Production (gdrive) zlm-v1-iab-classify-edge 0.302 0.531 0.385 gpt-5.4-nano 0.303 0.450 0.362 Independent gold (figure-eight) zlm-v1-iab-classify-edge 0.276 0.710 0.397 gpt-5.4-nano 0.260 0.721 0.382 Soft match — tier-1 hierarchical credit. Coverage: gdrive 6,993/7,216 · figure-eight 2,408/2,784. On the independent figure-eight gold set, zlm-v1-iab-classify-edge clears the Nano bar on hard-F1 ( 0.406 vs 0.374). ### 05 Speed & cost A production classifier must be fast and cheap as well as accurate. In production, zlm-v1-iab-classify-edge responds in 48 ms (p50) — about ~10× faster end-to-end than the incumbent — and is 3.3× cheaper per classification. Latency — measured in production System p50 p95 p99 Success zlm-v1-iab-classify-edge 48 ms 95 ms 197 ms 100% gpt-5.4-nano ~1,800–2,000 ms — — — About ~10× faster end-to-end. Measured against Dappier's live publisher network (ZeroGPU × Dappier case study, 2026); the head-to-head latency in this benchmark was recorded in a development environment and understates production. Price per 1M tokens System Input Output zlm-v1-iab-classify-edge $0.05 $0.40 gpt-5.4-nano $0.20 $1.25 Published list prices ($/1M tokens). At this benchmark's average request size (260 input + 100 output tokens), zlm-v1-iab-classify-edge works out 3.3× cheaper per classification than gpt-5.4-nano. Price vs. other production classifiers Blended cost per 1M tokens at the benchmark's 260-in / 100-out mix, against the cheapest small models from OpenAI, Google, and Anthropic — lower is better. zlm-v1-iab-classify-edge $0.147/1M ($0.05/$0.40 in/out) GPT-5.4 Nano $0.492/1M ($0.20/$1.25 in/out) · 3.3× zlm-v1-iab-classify-edge Gemini 2.5 Flash $0.911/1M ($0.30/$2.50 in/out) · 6.2× zlm-v1-iab-classify-edge Claude Haiku 4.5 $2.111/1M ($1.00/$5.00 in/out) · 14.3× zlm-v1-iab-classify-edge Sources: OpenAI, Google (ai.google.dev), and Anthropic published list prices (verified June 2026). zlm-v1-iab-classify-edge is 6.2× cheaper than Gemini 2.5 Flash and 14.3× cheaper than Claude Haiku 4.5. ### 06 Input efficiency — no system prompt, no prompt engineering A frontier model has to be told the entire IAB taxonomy and the output format on every request — thousands of tokens of instructions wrapped around each item. zlm-v1-iab-classify-edge has the taxonomy baked in: you send only the text. That is ~6× fewer input tokens per classification, and no prompt to maintain. ZeroGPU zlm-v1-iab-classify-edge — the entire request curl https://api.zerogpu.ai/v1/responses \ -H 'content-type: application/json' \ -H 'x-api-key: zgpu-api-•••••••••••• ' \ -H 'x-project-id: ••••••••-••••-•••• ' \ -d '{ "input" : "Technology has quietly reshaped the rhythm of everyday life, weaving itself into routines so seamlessly that it often goes unnoticed. From a smartphone alarm in the morning to the last glance at a glowing screen before sleep...", "model" : "zlm-v1-iab-classify-edge" }' ~400 input tokens. No system prompt, no taxonomy in the request, no few-shot examples — just the text and the model name. Frontier model — what it needs every call # system prompt — sent on every request You are a deterministic domain classifier. Classify the following content into the IAB v1 taxonomy. # + the full IAB content + audience taxonomy enumerated # + the required output schema and formatting rules # + the content to classify (often padded with retrieved context) Domain: cnn.com Website Content (truncated): Breaking News, Latest News and Videos | CNN ... ≈ 18 KB of instructions + content, re-sent every call ~2,000–3,000 input tokens. A full ~18 KB instruction + taxonomy + content prompt, re-sent and re-billed on every single request. Input-token figures from the ZeroGPU × Dappier case study, 2026 (400 vs ~2,000–3,000 tokens, ~6× fewer). Fewer input tokens means lower cost, lower latency, and nothing to prompt-engineer or keep in sync with taxonomy updates. ### 07 Reliability & output quality A production model must return valid, well-formed labels. zlm-v1-iab-classify-edge is constrained to the taxonomy's label space, so it never emits an off-taxonomy (hallucinated) label. Hallucination rate 0 % 0 hallucinated labels — vs gpt-5.4-nano, which invented off-taxonomy labels on 1,796 of 10,000 rows Labels returned per item 5.78 avg. IAB content categories assigned per item (plus 5.2 audience) — focused, high-confidence tagging, not a long noisy list Production response time 48 ms p50 · 95 ms p95 · 197 ms p99 · 100% success ### The bottom line Wins 66% of 9,592 blind head-to-head comparisons against gpt-5.4-nano. - Leads in 49 of 50 content areas shown, and on production traffic wins 71% of the time. - Clears the Nano bar on verified ground truth — figure-eight hard-F1 0.406 vs 0.374. - Hallucinated 0 labels across all 10,000 samples — vs gpt-5.4-nano's 1,796 rows with invented, off-taxonomy labels. - ~10× faster in production, 3.3× cheaper than gpt-5.4-nano (6× vs Gemini Flash, 14× vs Haiku), and uses ~6× fewer input tokens with no system prompt. ### Notes & limitations Fair-reading notes. Among the content areas shown, gpt-5.4-nano still leads in 1 (Career and Education). The win-rate gap is narrowest on the independent figure-eight gold set (52.9%) and widest on production traffic (71.0%). Latency is measured in production against Dappier's live publisher network (ZeroGPU × Dappier case study, 2026); the accuracy head-to-head above was run on a frozen test set in a development environment, whose latency understates production and is not reported here. Prices are published list prices (OpenAI, Google, Anthropic), blended at this benchmark's average request size. Run details Test set | 10,000 frozen English rows — production traffic + independent figure-eight gold set | Judge | gpt-5.5 — blind, pack size 25, seed 42 | ZeroGPU model | zlm-v1-iab-classify-edge — content + audience | Competitor | gpt-5.4-nano | Gold coverage | gdrive 6,993/7,216 · figure-eight 2,408/2,784 | Production latency | 48/95/197 ms p50/p95/p99 · 100% success (ZeroGPU × Dappier case study, 2026) | Hallucinated labels | zlm-v1-iab-classify-edge 0 · gpt-5.4-nano 1,796 | Generated | 2026-06-09T13:05:43 | --- # zlm-v2-iab-classify-edge-enriched: multilingual IAB classification at 4-9x lower latency > Benchmark of ZeroGPU's multilingual zlm-v2-iab-classify-edge-enriched model against GPT-5.4 Nano on 5,000 production prompts across 31 languages: accuracy, audience F1, and latency. Canonical URL: https://zerogpu.ai/benchmarks/iab-classify-v2 Benchmark · 5,000 production prompts · 31 languages · GPT-5.5 gold labels ## zlm-v2-iab-classify-edge-enriched: multilingual IAB classification at 4–9× lower latency ZeroGPU's multilingual, enrichment-aware classification model was evaluated against gpt-5.4-nano on 5,000 real production prompts — 3,000 of them non-English across 31 languages — using GPT-5.5-generated gold labels over the IAB Content 2.2 and Audience 1.1 taxonomies. Zero request errors. Median latency 238 ms 515 ms non-English · 4–9× faster than ~2.2 s Audience F1@5 0.292 vs gpt-5.4-nano 0.164 · stronger audience targeting Content top-1 0.657 within 11 points of gpt-5.4-nano (0.771) Coverage 31 langs 5,000 prompts · 0 request errors ### 01 How this benchmark was measured Both models saw the same 5,000 prompts under identical conditions, scored against a single frozen gold set and timed from the same co-located worker. 1 5,000 real production prompts Sampled from live orchestration prompt logs ( taskType=iab_classify ), deduplicated, median ~62 characters of short ad-copy and headline text. 3,000 of the rows are non-English, spanning 31 languages. 2 GPT-5.5 gold labels Gold labels were produced by gpt-5.5 , constrained to the exact IAB Content 2.2 (704 labels) and Audience 1.1 (1,567 labels) label space and validated locally. Gold reflects an LLM's judgement rather than certified human truth, so figures are best read as relative comparisons on identical data. 3 Co-located timing All requests were issued from a Fly worker in region iad , co-located with the classification API, so client network latency is negligible (wall − server ≈ 10–70 ms). Accuracy is measured over all 5,000 rows; latency separately at concurrency = 1 after warm-up. ### 02 IAB classification quality Top-1 = the model's first label is among the gold labels. F1@5 / precision@5 / recall@5 = set overlap over the returned lists. Tier-1 top-1 = the first tier-1 category matches gold. v2 lands within 11 points of gpt-5.4-nano on content top-1 and clearly ahead on audience targeting . Metric | zlm-v2-iab-classify-edge-enriched | ZLM v2 | gpt-5.4-nano | Content top-1 | | 0.657 | 0.771 | Content F1@5 | | 0.438 | 0.510 | Content precision@5 | | 0.408 | 0.475 | Content recall@5 | | 0.494 | 0.567 | Tier-1 top-1 | | 0.574 | 0.674 | Audience F1@5 | | 0.292 | 0.164 | Audience F1@5 is 78% higher than gpt-5.4-nano (0.292 vs 0.164), the metric that drives audience segmentation and targeting quality. ### 03 English vs non-English The multilingual path holds up on foreign-language traffic: v2 scores 0.647 top-1 on non-English content versus 0.671 on English — a 2.4-point spread. Metric | Set | ZLM v2 | gpt-5.4-nano | Content top-1 | English | 0.671 | 0.750 | Content top-1 | Non-English | 0.647 | 0.785 | Content F1@5 | English | 0.451 | 0.475 | Content F1@5 | Non-English | 0.429 | 0.533 | Audience F1@5 | English | 0.303 | 0.171 | Audience F1@5 | Non-English | 0.285 | 0.160 | ### Content top-1 by language Language | ZLM v2 top-1 | ZLM v2 | gpt-5.4-nano | English | | 0.671 | 0.750 | French | | 0.602 | 0.759 | German | | 0.619 | 0.757 | Italian | | 0.844 | 0.908 | Spanish | | 0.587 | 0.741 | Hebrew | | 0.549 | 0.750 | Dutch | | 0.578 | 0.656 | Portuguese | | 0.586 | 0.741 | ### 04 Accuracy by content area Each prompt is bucketed by its gold top-1 tier-1 category. Buckets with at least 25 prompts, sorted by volume. n = prompts in the bucket. Green bars mark the areas where v2 leads on top-1. Category | n | ZLM v2 top-1 | ZLM v2 | GPT top-1 | ZLM F1@5 | GPT F1@5 | Real Estate | 592 | | 0.836 | 0.921 | 0.655 | 0.730 | Medical Health | 503 | | 0.738 | 0.841 | 0.440 | 0.529 | Business and Finance | 396 | | 0.646 | 0.795 | 0.398 | 0.477 | Personal Finance | 358 | | 0.682 | 0.679 | 0.461 | 0.472 | Home & Garden | 279 | | 0.642 | 0.788 | 0.461 | 0.469 | Pop Culture | 252 | | 0.436 | 0.631 | 0.337 | 0.436 | Healthy Living | 241 | | 0.635 | 0.813 | 0.457 | 0.553 | News and Politics | 225 | | 0.707 | 0.822 | 0.391 | 0.474 | Style & Fashion | 216 | | 0.556 | 0.616 | 0.399 | 0.476 | Automotive | 211 | | 0.621 | 0.739 | 0.407 | 0.475 | Technology & Computing | 210 | | 0.767 | 0.819 | 0.524 | 0.493 | Travel | 198 | | 0.702 | 0.828 | 0.426 | 0.491 | Shopping | 136 | | 0.507 | 0.691 | 0.397 | 0.509 | Sports | 126 | | 0.786 | 0.794 | 0.412 | 0.500 | Family and Relationships | 114 | | 0.614 | 0.772 | 0.458 | 0.476 | Pets | 111 | | 0.757 | 0.739 | 0.482 | 0.400 | Food & Drink | 108 | | 0.750 | 0.824 | 0.506 | 0.546 | Education | 97 | | 0.660 | 0.835 | 0.402 | 0.503 | Hobbies & Interests | 75 | | 0.560 | 0.640 | 0.343 | 0.481 | Events and Attractions | 59 | | 0.610 | 0.678 | 0.364 | 0.439 | Careers | 51 | | 0.392 | 0.765 | 0.302 | 0.469 | Science | 40 | | 0.725 | 0.800 | 0.474 | 0.506 | Television | 39 | | 0.538 | 0.692 | 0.265 | 0.549 | Books and Literature | 37 | | 0.460 | 0.757 | 0.309 | 0.482 | Movies | 36 | | 0.861 | 0.833 | 0.364 | 0.566 | Fine Art | 29 | | 0.517 | 0.655 | 0.301 | 0.493 | ### 05 Latency Measured warm, one request at a time (concurrency = 1) — the latency a single production caller experiences. ZLM figures are the server-reported processing_time_ms ; gpt-5.4-nano has no server timing, so its client wall-clock from the same co-located worker is used. Model | Set | p50 | p90 | p95 | mean | ZLM v2 edge-enriched | English | 238 ms | 348 ms | 370 ms | 243 ms | ZLM v2 edge-enriched | Non-English | 515 ms | 856 ms | 1061 ms | 548 ms | gpt-5.4-nano | English | 2232 ms | 3169 ms | 3505 ms | 3381 ms | gpt-5.4-nano | Non-English | 2173 ms | 3107 ms | 3656 ms | 2510 ms | ### Median speed-up English 9 × 238 ms vs 2,232 ms Non-English 4 × 515 ms vs 2,173 ms Request errors 0 across all 5,000 prompts ZLM v2 responds in 238 ms (English) / 515 ms (non-English) at the median, versus ~2.2 s for gpt-5.4-nano — a 4–9× latency advantage while trailing GPT on top-1 accuracy by a small margin. ### The bottom line - 4–9× faster at the median than gpt-5.4-nano, on both English and non-English traffic. - Audience F1@5 0.292 vs 0.164 — materially stronger audience signal extraction. - Content top-1 0.657 vs 0.771 — within 11 points of gpt-5.4-nano on the same gold labels. - Consistent across languages : 0.647 top-1 on non-English vs 0.671 on English, across 31 languages. - Leads gpt-5.4-nano on top-1 in Personal Finance, Pets and Movies, and on F1@5 in Technology & Computing and Pets. ### Notes & limitations Fair-reading notes. gpt-5.4-nano is included as a reference point and remains ahead on content top-1 in most categories. Gold labels are GPT-5.5 judgements rather than certified human truth, so the absolute numbers should be read as relative comparisons on identical data. Latency for gpt-5.4-nano is client wall-clock from a co-located worker, while ZLM figures are server-reported processing time. Run details Test set | 5,000 real production prompts · 3,000 non-English across 31 languages · median ~62 chars | Taxonomies | IAB Content 2.2 (704 labels) · IAB Audience 1.1 (1,567 labels) | Gold labels | gpt-5.5, constrained to the exact label space, validated locally | ZeroGPU model | zlm-v2-iab-classify-edge-enriched — multilingual, content + audience | Reference model | gpt-5.4-nano | Environment | Fly worker, region iad, co-located with the classification API | Latency protocol | warm, concurrency = 1, split by English / non-English | Date | 2026-08-03 | --- # zlm-v1-moderation-edge vs OpenAI omni-moderation: moderation benchmark > Head-to-head benchmark of ZeroGPU's zlm-v1-moderation-edge against OpenAI omni-moderation: 0.899 vs 0.853 binary F1, wins on 9 of 13 harm categories, and 1.2-1.8x faster p50 latency on production-range inputs. Canonical URL: https://zerogpu.ai/benchmarks/moderation-edge zlm-v1-moderation-edge is ZeroGPU's purpose-built content moderation model, benchmarked head to head against OpenAI omni-moderation: 0.899 vs 0.853 binary F1, wins on 9 of 13 harm categories, and 1.2-1.8x faster p50 latency on production-range inputs. It returns an OpenAI-compatible moderation response shape, so existing moderation pipelines can switch by changing the endpoint and model name. Capacity scales with the ZeroGPU edge network. --- # Blog | ZeroGPU > Latest news, research, and updates from ZeroGPU on distributed AI inference, edge compute, and Nano Language Models. Canonical URL: https://zerogpu.ai/blog --- # ZeroGPU Savings Calculator: Compare AI Inference Costs > Compare estimated monthly token costs between ZeroGPU models and OpenAI, Anthropic, Google. See how much you can save. 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They continue using your bot normally while you earn revenue in the background. ### What about user privacy and data? We take privacy seriously. No user data is collected or transmitted. The SDK only uses computational resources during idle periods to process AI inference tasks. ### How much can I earn with my Telegram bot? Earnings depend on your user base size and bot activity patterns. Bots with larger, more active user bases typically generate more revenue. Join the waitlist to get early access and personalized earning estimates. --- # Privacy Policy - ZeroGPU > Privacy Policy for ZeroGPU - Learn how we collect, use, and protect your personal information. Canonical URL: https://zerogpu.ai/privacy-policy --- # Monetization and Edge Operator Terms - ZeroGPU > Monetization and Edge Operator Terms for ZeroGPU: terms governing participation in the ZeroGPU monetization and supply network. Canonical URL: https://zerogpu.ai/supply-terms --- # Terms of Service - ZeroGPU > Terms of Service for ZeroGPU - Read our terms and conditions governing access to and use of our services. Canonical URL: https://zerogpu.ai/terms --- # AI Inference Use Cases | ZeroGPU Solutions for Every Industry > Discover how companies across industries use ZeroGPU for cost-effective AI inference. From ad tech to healthcare, explore real-world use cases and success stories. Canonical URL: https://zerogpu.ai/use-cases --- # Real-Time IAB Content Classification for Ad Tech | ZeroGPU > Classify content and conversation context into monetization signals for ad partners. Sub-100ms edge inference with IAB taxonomy mapping. Purpose-built 90M parameter ONNX models. Canonical URL: https://zerogpu.ai/use-cases/adtech-intent-classification ## Services - **zlm-v1-iab-classify-edge**: Purpose-built IAB taxonomy classifier for real-time content categorization at the edge. Maps text to the IAB Content Taxonomy, the industry standard across programmatic advertising. At 90M parameters on ONNX, it's optimized for high-volume, sub-100ms classification without roundtrips to a centralized server. - **zlm-v1-iab-classify-edge-enriched**: The enriched variant goes beyond standard taxonomy labels by producing multi-signal outputs, not just a category but layered context signals that downstream systems can use for better targeting, filtering, and personalization. Same 90M-parameter ONNX architecture, same sub-100ms edge speeds. ## FAQ ### What is the IAB Content Taxonomy? The IAB Content Taxonomy is the industry-standard classification system used across programmatic advertising. It categorizes web content into hierarchical categories that ad platforms use for contextual targeting and brand safety. ZeroGPU's models map content directly to this taxonomy. ### What's the difference between the base and enriched models? The base model (zlm-v1-iab-classify-edge) returns IAB category labels. The enriched model (zlm-v1-iab-classify-edge-enriched) returns multi-signal outputs (layered context signals beyond just a category), giving your downstream systems richer data for targeting, filtering, and personalization decisions. ### What are the response times? Both models deliver sub-100ms response times through edge processing. At 90M parameters on ONNX, they're optimized for the high-volume classification that ad tech platforms demand, with no roundtrips to centralized servers required. ### How do I use these models for ad monetization? Classify content or conversation context in real-time to generate IAB category signals and contextual data. These signals can be passed to ad partners in bid requests, enabling more precise contextual targeting and higher-value impressions without relying on third-party cookies. ### Can I deploy these models on edge devices? Yes. Both models are designed for edge deployment. The 90M-parameter ONNX architecture runs efficiently on edge infrastructure, enabling in-stream classification at the point of interaction without centralized server dependencies. ### How does ZeroGPU scale for high-volume ad tech workloads? ZeroGPU's distributed architecture automatically scales to handle any volume, from thousands to billions of classification requests. Our edge-native approach eliminates capacity planning overhead, so you can focus on building rather than infrastructure. ### How do I integrate ZeroGPU into my ad tech stack? Integration is straightforward via our REST API, Python SDK, or JavaScript SDK. Most developers get up and running in under an hour. We provide comprehensive documentation and code examples for common ad tech integration patterns. --- # AI Agent Tool Planning & Reasoning | ZeroGPU Inference API > Power autonomous AI agents with fast tool selection, multi-step reasoning, and task decomposition. Low-latency inference for real-time agent workflows. Canonical URL: https://zerogpu.ai/use-cases/agent-tool-planning ## Services - **Tool Selection Service**: Classify user intent and select the optimal tool or API from a registered toolset - **Reasoning & Planning Service**: Break down complex tasks into executable steps with dependency-aware planning ## FAQ ### How does ZeroGPU's distributed inference work? ZeroGPU distributes AI inference across idle GPU capacity worldwide, creating a decentralized network that's faster and more cost-effective than traditional GPU clouds. Your requests are routed to the nearest available GPU for optimal performance. ### Can ZeroGPU handle multi-step agent workflows? Yes. ZeroGPU supports multi-step reasoning where each step can invoke different nano models for tool selection, parameter extraction, and result validation. The low latency makes chained inference practical for real-time agent interactions. ### How do I register tools for the agent to use? You define tools as JSON schemas describing their name, description, and parameters. ZeroGPU's classification models use these definitions to match user intents to the right tool, similar to OpenAI's function calling format. ### What's the typical latency with ZeroGPU? ZeroGPU delivers sub-100ms latency for most inference tasks through edge processing and smart routing. For agent workflows, each reasoning step adds minimal overhead, enabling real-time multi-step execution. ### How does ZeroGPU scale as my usage grows? ZeroGPU automatically scales to handle any volume, from thousands to billions of requests. Our distributed network grows with demand, so you never have to worry about provisioning infrastructure or capacity planning. --- # AI Clinical Decision Support & Patient Triage | ZeroGPU Medical API > Power clinical decision support systems and patient triage with AI. Real-time symptom analysis, risk scoring, and care recommendations at 10x lower cost with ZeroGPU. Canonical URL: https://zerogpu.ai/use-cases/clinical-decision-support-triage ## Services - **Classification Service**: Triage patient symptoms by urgency and care level - **Summarization Service**: Summarize patient history from electronic health records ## FAQ ### How does ZeroGPU's distributed inference work? ZeroGPU distributes AI inference across idle GPU capacity worldwide, creating a decentralized network that's faster and more cost-effective than traditional GPU clouds. Your requests are routed to the nearest available GPU for optimal performance. ### What's the typical latency with ZeroGPU? ZeroGPU delivers sub-100ms latency for most inference tasks through edge processing and smart routing. Latency varies by model size and complexity, but our distributed architecture ensures consistently fast response times. ### How do I integrate ZeroGPU into my application? Integration is simple via our REST API, Python SDK, or JavaScript SDK. Most developers get up and running in under an hour. We provide comprehensive documentation, code examples, and support for popular frameworks. ### How does ZeroGPU scale as my usage grows? ZeroGPU automatically scales to handle any volume, from thousands to billions of requests. Our distributed network grows with demand, so you never have to worry about provisioning infrastructure or capacity planning. ### Can I use custom or fine-tuned models with ZeroGPU? Yes. ZeroGPU supports custom models and fine-tuning. You can bring your own models, fine-tune our base models on your data, or use our pre-trained models. We support popular frameworks like PyTorch, TensorFlow, and Hugging Face. ### How does ZeroGPU compare to traditional GPU clouds? ZeroGPU is significantly more cost-effective than traditional GPU clouds (AWS, GCP, Azure) while delivering comparable or better performance. Our distributed architecture eliminates idle capacity waste and reduces infrastructure overhead. ### What makes ZeroGPU's edge processing unique? ZeroGPU processes inference requests at the edge, closer to your users, which reduces latency and enables real-time AI applications. This edge-first approach also provides better privacy and data sovereignty options. --- # AI Content Moderation at Scale | ZeroGPU Toxicity Detection API > Moderate user-generated content at scale with AI toxicity detection, NSFW filtering, and policy compliance. Real-time inference at 1/10th the cost. Canonical URL: https://zerogpu.ai/use-cases/content-moderation ## Services - **zlm-v1-moderation-edge**: Purpose-built text moderation model that detects harmful content across 13 policy categories in real time. Optimized for high-volume, low-latency inference on the ZeroGPU edge network. ## FAQ ### What model powers ZeroGPU content moderation? ZeroGPU content moderation is powered by zlm-v1-moderation-edge, a specialized edge model trained to detect harmful content across 13 policy categories. It returns per-category confidence scores in an OpenAI-compatible format, so it drops into existing moderation pipelines with minimal changes. ### What's the typical latency with ZeroGPU? ZeroGPU delivers sub-100ms latency for most moderation requests through edge processing. Latency varies by payload size and complexity, but the zlm-v1-moderation-edge model is optimized for high-volume, real-time content moderation. ### How do I integrate ZeroGPU into my application? Integration is simple via our REST API, Python SDK, or JavaScript SDK. The moderation endpoint uses an OpenAI-compatible response shape, so most developers get up and running in under an hour. We provide comprehensive documentation, code examples, and support for popular frameworks. ### How does ZeroGPU scale as my usage grows? ZeroGPU automatically scales to handle any volume, from thousands to billions of requests. Our distributed edge network grows with demand, so you never have to worry about provisioning infrastructure or capacity planning. ### Can I use custom or fine-tuned models with ZeroGPU? Yes. ZeroGPU supports custom models and fine-tuning. You can bring your own models, fine-tune our base models on your data, or use our pre-trained models like zlm-v1-moderation-edge. We support popular frameworks like PyTorch, TensorFlow, and Hugging Face. ### How does ZeroGPU compare to traditional GPU clouds? ZeroGPU is significantly more cost-effective than traditional GPU clouds (AWS, GCP, Azure) while delivering comparable or better performance. Our distributed architecture eliminates idle capacity waste and reduces infrastructure overhead. ### What makes ZeroGPU's edge processing unique? ZeroGPU processes inference requests at the edge, closer to your users, which reduces latency and enables real-time AI applications. This edge-first approach also provides better privacy and data sovereignty options. --- # AI Customer Support Automation | ZeroGPU Chatbot Inference API > Automate ticket routing, generate responses, and resolve issues faster with AI-powered support. Reduce costs by 70% with ZeroGPU inference. Canonical URL: https://zerogpu.ai/use-cases/customer-support-automation ## Services - **Classification Service**: Categorize support tickets by issue type, priority, and department routing - **Summarization Service**: Summarize long customer conversations and ticket histories ## FAQ ### How does ZeroGPU's distributed inference work? ZeroGPU distributes AI inference across idle GPU capacity worldwide, creating a decentralized network that's faster and more cost-effective than traditional GPU clouds. Your requests are routed to the nearest available GPU for optimal performance. ### What's the typical latency with ZeroGPU? ZeroGPU delivers sub-100ms latency for most inference tasks through edge processing and smart routing. Latency varies by model size and complexity, but our distributed architecture ensures consistently fast response times. ### How do I integrate ZeroGPU into my application? Integration is simple via our REST API, Python SDK, or JavaScript SDK. Most developers get up and running in under an hour. We provide comprehensive documentation, code examples, and support for popular frameworks. ### How does ZeroGPU scale as my usage grows? ZeroGPU automatically scales to handle any volume, from thousands to billions of requests. Our distributed network grows with demand, so you never have to worry about provisioning infrastructure or capacity planning. ### Can I use custom or fine-tuned models with ZeroGPU? Yes. ZeroGPU supports custom models and fine-tuning. You can bring your own models, fine-tune our base models on your data, or use our pre-trained models. We support popular frameworks like PyTorch, TensorFlow, and Hugging Face. ### How does ZeroGPU compare to traditional GPU clouds? ZeroGPU is significantly more cost-effective than traditional GPU clouds (AWS, GCP, Azure) while delivering comparable or better performance. Our distributed architecture eliminates idle capacity waste and reduces infrastructure overhead. ### What makes ZeroGPU's edge processing unique? ZeroGPU processes inference requests at the edge, closer to your users, which reduces latency and enables real-time AI applications. This edge-first approach also provides better privacy and data sovereignty options. --- # AI Data Enrichment & Lead Scoring | ZeroGPU Profile Enhancement API > Enrich customer profiles with AI-powered lead scoring, company data, and behavioral insights. Fast, scalable inference for sales and marketing teams. Canonical URL: https://zerogpu.ai/use-cases/data-enrichment ## Services - **Embedding Service**: Generate vector embeddings for semantic search and similarity matching - **Web Scraping Service**: Extract company data from websites for CRM enrichment ## FAQ ### How does ZeroGPU's distributed inference work? ZeroGPU distributes AI inference across idle GPU capacity worldwide, creating a decentralized network that's faster and more cost-effective than traditional GPU clouds. Your requests are routed to the nearest available GPU for optimal performance. ### What's the typical latency with ZeroGPU? ZeroGPU delivers sub-100ms latency for most inference tasks through edge processing and smart routing. Latency varies by model size and complexity, but our distributed architecture ensures consistently fast response times. ### How do I integrate ZeroGPU into my application? Integration is simple via our REST API, Python SDK, or JavaScript SDK. Most developers get up and running in under an hour. We provide comprehensive documentation, code examples, and support for popular frameworks. ### How does ZeroGPU scale as my usage grows? ZeroGPU automatically scales to handle any volume, from thousands to billions of requests. Our distributed network grows with demand, so you never have to worry about provisioning infrastructure or capacity planning. ### Can I use custom or fine-tuned models with ZeroGPU? Yes. ZeroGPU supports custom models and fine-tuning. You can bring your own models, fine-tune our base models on your data, or use our pre-trained models. We support popular frameworks like PyTorch, TensorFlow, and Hugging Face. ### How does ZeroGPU compare to traditional GPU clouds? ZeroGPU is significantly more cost-effective than traditional GPU clouds (AWS, GCP, Azure) while delivering comparable or better performance. Our distributed architecture eliminates idle capacity waste and reduces infrastructure overhead. ### What makes ZeroGPU's edge processing unique? ZeroGPU processes inference requests at the edge, closer to your users, which reduces latency and enables real-time AI applications. This edge-first approach also provides better privacy and data sovereignty options. --- # AI Document Intelligence & OCR | ZeroGPU Invoice Extraction API > Extract structured data from invoices, contracts, and forms with AI-powered OCR. Process documents 10x cheaper with ZeroGPU's inference API. Canonical URL: https://zerogpu.ai/use-cases/document-intelligence-ocr ## Services - **Vision OCR Service**: Extract text from documents with high accuracy OCR - **Classification Service**: Classify document types for routing and processing ## FAQ ### How does ZeroGPU's distributed inference work? ZeroGPU distributes AI inference across idle GPU capacity worldwide, creating a decentralized network that's faster and more cost-effective than traditional GPU clouds. Your requests are routed to the nearest available GPU for optimal performance. ### What's the typical latency with ZeroGPU? ZeroGPU delivers sub-100ms latency for most inference tasks through edge processing and smart routing. Latency varies by model size and complexity, but our distributed architecture ensures consistently fast response times. ### How do I integrate ZeroGPU into my application? Integration is simple via our REST API, Python SDK, or JavaScript SDK. Most developers get up and running in under an hour. We provide comprehensive documentation, code examples, and support for popular frameworks. ### How does ZeroGPU scale as my usage grows? ZeroGPU automatically scales to handle any volume, from thousands to billions of requests. Our distributed network grows with demand, so you never have to worry about provisioning infrastructure or capacity planning. ### Can I use custom or fine-tuned models with ZeroGPU? Yes. ZeroGPU supports custom models and fine-tuning. You can bring your own models, fine-tune our base models on your data, or use our pre-trained models. We support popular frameworks like PyTorch, TensorFlow, and Hugging Face. ### How does ZeroGPU compare to traditional GPU clouds? ZeroGPU is significantly more cost-effective than traditional GPU clouds (AWS, GCP, Azure) while delivering comparable or better performance. Our distributed architecture eliminates idle capacity waste and reduces infrastructure overhead. ### What makes ZeroGPU's edge processing unique? ZeroGPU processes inference requests at the edge, closer to your users, which reduces latency and enables real-time AI applications. This edge-first approach also provides better privacy and data sovereignty options. --- # AI Email Intelligence & Triage | ZeroGPU Automation API > Automatically classify email intent (sales, support, pitch) and route conversations with AI. Save 80% on email processing costs with ZeroGPU. Canonical URL: https://zerogpu.ai/use-cases/email-intelligence-triage ## Services - **Classification Service**: Categorize emails by urgency, department, and topic for intelligent routing - **Summarization Service**: Generate concise email summaries for quick triage ## FAQ ### How does ZeroGPU's distributed inference work? ZeroGPU distributes AI inference across idle GPU capacity worldwide, creating a decentralized network that's faster and more cost-effective than traditional GPU clouds. Your requests are routed to the nearest available GPU for optimal performance. ### What's the typical latency with ZeroGPU? ZeroGPU delivers sub-100ms latency for most inference tasks through edge processing and smart routing. Latency varies by model size and complexity, but our distributed architecture ensures consistently fast response times. ### How do I integrate ZeroGPU into my application? Integration is simple via our REST API, Python SDK, or JavaScript SDK. Most developers get up and running in under an hour. We provide comprehensive documentation, code examples, and support for popular frameworks. ### How does ZeroGPU scale as my usage grows? ZeroGPU automatically scales to handle any volume, from thousands to billions of requests. Our distributed network grows with demand, so you never have to worry about provisioning infrastructure or capacity planning. ### Can I use custom or fine-tuned models with ZeroGPU? Yes. ZeroGPU supports custom models and fine-tuning. You can bring your own models, fine-tune our base models on your data, or use our pre-trained models. We support popular frameworks like PyTorch, TensorFlow, and Hugging Face. ### How does ZeroGPU compare to traditional GPU clouds? ZeroGPU is significantly more cost-effective than traditional GPU clouds (AWS, GCP, Azure) while delivering comparable or better performance. Our distributed architecture eliminates idle capacity waste and reduces infrastructure overhead. ### What makes ZeroGPU's edge processing unique? ZeroGPU processes inference requests at the edge, closer to your users, which reduces latency and enables real-time AI applications. This edge-first approach also provides better privacy and data sovereignty options. --- # AI Fraud Detection & Risk Scoring | ZeroGPU Transaction Monitoring API > Detect fraud and score transaction risk in real-time with AI. Protect your platform with ZeroGPU's low-latency, cost-effective inference API. Canonical URL: https://zerogpu.ai/use-cases/fraud-detection-risk-scoring ## Services - **Classification Service**: Detect fraudulent patterns and anomalies in transactions - **Embedding Service**: Create transaction embeddings for fraud similarity detection ## FAQ ### How does ZeroGPU's distributed inference work? ZeroGPU distributes AI inference across idle GPU capacity worldwide, creating a decentralized network that's faster and more cost-effective than traditional GPU clouds. Your requests are routed to the nearest available GPU for optimal performance. ### What's the typical latency with ZeroGPU? ZeroGPU delivers sub-100ms latency for most inference tasks through edge processing and smart routing. Latency varies by model size and complexity, but our distributed architecture ensures consistently fast response times. ### How do I integrate ZeroGPU into my application? Integration is simple via our REST API, Python SDK, or JavaScript SDK. Most developers get up and running in under an hour. We provide comprehensive documentation, code examples, and support for popular frameworks. ### How does ZeroGPU scale as my usage grows? ZeroGPU automatically scales to handle any volume, from thousands to billions of requests. Our distributed network grows with demand, so you never have to worry about provisioning infrastructure or capacity planning. ### Can I use custom or fine-tuned models with ZeroGPU? Yes. ZeroGPU supports custom models and fine-tuning. You can bring your own models, fine-tune our base models on your data, or use our pre-trained models. We support popular frameworks like PyTorch, TensorFlow, and Hugging Face. ### How does ZeroGPU compare to traditional GPU clouds? ZeroGPU is significantly more cost-effective than traditional GPU clouds (AWS, GCP, Azure) while delivering comparable or better performance. Our distributed architecture eliminates idle capacity waste and reduces infrastructure overhead. ### What makes ZeroGPU's edge processing unique? ZeroGPU processes inference requests at the edge, closer to your users, which reduces latency and enables real-time AI applications. This edge-first approach also provides better privacy and data sovereignty options. --- # Jailbreak & Prompt Injection Detection | ZeroGPU Inference API > Detect and block prompt injection and jailbreak attacks in real-time. Protect your LLM applications with ZeroGPU's distributed AI safety layer. Canonical URL: https://zerogpu.ai/use-cases/jailbreak-detection ## Services - **Prompt Injection Detection**: Classify and score prompts for injection attacks, jailbreak attempts, and adversarial inputs - **Output Safety Guard**: Validate LLM outputs for policy violations, leaked system prompts, and harmful content ## FAQ ### How does ZeroGPU's distributed inference work? ZeroGPU distributes AI inference across idle GPU capacity worldwide, creating a decentralized network that's faster and more cost-effective than traditional GPU clouds. Your requests are routed to the nearest available GPU for optimal performance. ### What types of prompt attacks can ZeroGPU detect? ZeroGPU detects direct prompt injection, indirect injection via external content, role-playing jailbreaks, encoding-based bypasses, and multi-turn manipulation attempts. Our models are continuously updated against new attack vectors. ### Can I customize detection sensitivity? Yes. You can configure sensitivity thresholds to balance between strictness and user experience. High-security applications like banking can use aggressive filtering, while creative tools can use lighter guardrails. ### How does ZeroGPU scale as my usage grows? ZeroGPU automatically scales to handle any volume, from thousands to billions of requests. Our distributed network grows with demand, so you never have to worry about provisioning infrastructure or capacity planning. ### What's the typical latency with ZeroGPU? ZeroGPU delivers sub-100ms latency for most inference tasks through edge processing and smart routing. Prompt safety checks typically complete in under 50ms, adding minimal overhead to your pipeline. --- # Multimodal AI Inference | Image + Text | ZeroGPU Inference API > Run multimodal AI inference combining image and text inputs. Visual classification, document understanding, and VQA with ZeroGPU's distributed edge network. Canonical URL: https://zerogpu.ai/use-cases/multimodal-inference ## Services - **Vision Classification Service**: Classify images and extract visual features for downstream AI tasks - **Multimodal Understanding Service**: Combine image and text inputs for richer AI inference and document understanding ## FAQ ### How does ZeroGPU's distributed inference work? ZeroGPU distributes AI inference across idle GPU capacity worldwide, creating a decentralized network that's faster and more cost-effective than traditional GPU clouds. Your requests are routed to the nearest available GPU for optimal performance. ### What image formats does ZeroGPU support? ZeroGPU supports JPEG, PNG, WebP, and GIF inputs. Images can be passed as base64-encoded strings or URLs. We automatically resize and optimize images for inference without losing classification accuracy. ### Can I combine image and text in a single request? Yes. ZeroGPU supports multimodal inputs where you send both an image and text prompt in a single API call. This enables use cases like visual question answering, image captioning, and document understanding. ### What's the typical latency with ZeroGPU? ZeroGPU delivers sub-100ms latency for most inference tasks through edge processing and smart routing. Multimodal tasks may take slightly longer depending on image size, but our edge architecture keeps response times consistently fast. ### How does ZeroGPU scale as my usage grows? ZeroGPU automatically scales to handle any volume, from thousands to billions of requests. Our distributed network grows with demand, so you never have to worry about provisioning infrastructure or capacity planning. --- # AI Personalization Engine | ZeroGPU Recommendation API > Build recommendation engines and dynamic content personalization with AI. Deliver 1:1 experiences at scale with ZeroGPU's distributed inference. Canonical URL: https://zerogpu.ai/use-cases/personalization-engine ## Services - **Embedding Service**: Create user and content embeddings for personalized recommendations - **Classification Service**: Classify user interests and behavior for segmentation ## FAQ ### How does ZeroGPU's distributed inference work? ZeroGPU distributes AI inference across idle GPU capacity worldwide, creating a decentralized network that's faster and more cost-effective than traditional GPU clouds. Your requests are routed to the nearest available GPU for optimal performance. ### What's the typical latency with ZeroGPU? ZeroGPU delivers sub-100ms latency for most inference tasks through edge processing and smart routing. Latency varies by model size and complexity, but our distributed architecture ensures consistently fast response times. ### How do I integrate ZeroGPU into my application? Integration is simple via our REST API, Python SDK, or JavaScript SDK. Most developers get up and running in under an hour. We provide comprehensive documentation, code examples, and support for popular frameworks. ### How does ZeroGPU scale as my usage grows? ZeroGPU automatically scales to handle any volume, from thousands to billions of requests. Our distributed network grows with demand, so you never have to worry about provisioning infrastructure or capacity planning. ### Can I use custom or fine-tuned models with ZeroGPU? Yes. ZeroGPU supports custom models and fine-tuning. You can bring your own models, fine-tune our base models on your data, or use our pre-trained models. We support popular frameworks like PyTorch, TensorFlow, and Hugging Face. ### How does ZeroGPU compare to traditional GPU clouds? ZeroGPU is significantly more cost-effective than traditional GPU clouds (AWS, GCP, Azure) while delivering comparable or better performance. Our distributed architecture eliminates idle capacity waste and reduces infrastructure overhead. ### What makes ZeroGPU's edge processing unique? ZeroGPU processes inference requests at the edge, closer to your users, which reduces latency and enables real-time AI applications. This edge-first approach also provides better privacy and data sovereignty options. --- # PII Redaction & Data Masking | ZeroGPU Inference API > Detect and redact personally identifiable information in real-time. GDPR, HIPAA, and CCPA compliant PII masking with ZeroGPU's distributed inference. Canonical URL: https://zerogpu.ai/use-cases/pii-redaction ## Services - **PII Detection Service**: Detect and classify personally identifiable information across text, documents, and structured data - **Data Masking Service**: Automatically mask, tokenize, or anonymize sensitive data while preserving utility ## FAQ ### How does ZeroGPU's distributed inference work? ZeroGPU distributes AI inference across idle GPU capacity worldwide, creating a decentralized network that's faster and more cost-effective than traditional GPU clouds. Your requests are routed to the nearest available GPU for optimal performance. ### What's the typical latency with ZeroGPU? ZeroGPU delivers sub-100ms latency for most inference tasks through edge processing and smart routing. Latency varies by model size and complexity, but our distributed architecture ensures consistently fast response times. ### Is PII redaction compliant with data privacy regulations? Yes. ZeroGPU's PII redaction supports GDPR, HIPAA, and CCPA compliance workflows. Data can be processed at the edge to minimize cross-border transfers, and all redaction operations are logged for audit purposes. ### Can I define custom PII entity types? Absolutely. Beyond standard entities like names, emails, and SSNs, you can define custom entity patterns for industry-specific data such as medical record numbers, policy IDs, or proprietary identifiers. ### How does ZeroGPU scale as my usage grows? ZeroGPU automatically scales to handle any volume, from thousands to billions of requests. Our distributed network grows with demand, so you never have to worry about provisioning infrastructure or capacity planning. --- # AI Sentiment Analysis for Trading | ZeroGPU Market Sentiment API > Analyze market sentiment from news, social media, and reports in real-time. Power trading algorithms with ZeroGPU's low-latency AI inference. Canonical URL: https://zerogpu.ai/use-cases/sentiment-analysis-trading ## Services - **Classification Service**: Classify sentiment from financial text and news - **Summarization Service**: Summarize earnings calls and financial reports ## FAQ ### How does ZeroGPU's distributed inference work? ZeroGPU distributes AI inference across idle GPU capacity worldwide, creating a decentralized network that's faster and more cost-effective than traditional GPU clouds. Your requests are routed to the nearest available GPU for optimal performance. ### What's the typical latency with ZeroGPU? ZeroGPU delivers sub-100ms latency for most inference tasks through edge processing and smart routing. Latency varies by model size and complexity, but our distributed architecture ensures consistently fast response times. ### How do I integrate ZeroGPU into my application? Integration is simple via our REST API, Python SDK, or JavaScript SDK. Most developers get up and running in under an hour. We provide comprehensive documentation, code examples, and support for popular frameworks. ### How does ZeroGPU scale as my usage grows? ZeroGPU automatically scales to handle any volume, from thousands to billions of requests. Our distributed network grows with demand, so you never have to worry about provisioning infrastructure or capacity planning. ### Can I use custom or fine-tuned models with ZeroGPU? Yes. ZeroGPU supports custom models and fine-tuning. You can bring your own models, fine-tune our base models on your data, or use our pre-trained models. We support popular frameworks like PyTorch, TensorFlow, and Hugging Face. ### How does ZeroGPU compare to traditional GPU clouds? ZeroGPU is significantly more cost-effective than traditional GPU clouds (AWS, GCP, Azure) while delivering comparable or better performance. Our distributed architecture eliminates idle capacity waste and reduces infrastructure overhead. ### What makes ZeroGPU's edge processing unique? ZeroGPU processes inference requests at the edge, closer to your users, which reduces latency and enables real-time AI applications. This edge-first approach also provides better privacy and data sovereignty options. --- # AI Translation & Localization | ZeroGPU Multi-Language API > Translate content across 100+ languages with AI. Fast, accurate localization for global apps at 1/10th the cost of traditional GPU inference. Canonical URL: https://zerogpu.ai/use-cases/translation-localization ## Services - **Translation Service**: Translate text across 100+ languages with neural machine translation - **Summarization Service**: Create localized summaries adapted for different markets ## FAQ ### How does ZeroGPU's distributed inference work? ZeroGPU distributes AI inference across idle GPU capacity worldwide, creating a decentralized network that's faster and more cost-effective than traditional GPU clouds. Your requests are routed to the nearest available GPU for optimal performance. ### What's the typical latency with ZeroGPU? ZeroGPU delivers sub-100ms latency for most inference tasks through edge processing and smart routing. Latency varies by model size and complexity, but our distributed architecture ensures consistently fast response times. ### How do I integrate ZeroGPU into my application? Integration is simple via our REST API, Python SDK, or JavaScript SDK. Most developers get up and running in under an hour. We provide comprehensive documentation, code examples, and support for popular frameworks. ### How does ZeroGPU scale as my usage grows? ZeroGPU automatically scales to handle any volume, from thousands to billions of requests. Our distributed network grows with demand, so you never have to worry about provisioning infrastructure or capacity planning. ### Can I use custom or fine-tuned models with ZeroGPU? Yes. ZeroGPU supports custom models and fine-tuning. You can bring your own models, fine-tune our base models on your data, or use our pre-trained models. We support popular frameworks like PyTorch, TensorFlow, and Hugging Face. ### How does ZeroGPU compare to traditional GPU clouds? ZeroGPU is significantly more cost-effective than traditional GPU clouds (AWS, GCP, Azure) while delivering comparable or better performance. Our distributed architecture eliminates idle capacity waste and reduces infrastructure overhead. ### What makes ZeroGPU's edge processing unique? ZeroGPU processes inference requests at the edge, closer to your users, which reduces latency and enables real-time AI applications. This edge-first approach also provides better privacy and data sovereignty options.