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Waypoint-1.5: Higher-Fidelity Interactive Worlds for Everyday GPUs ALTK‑Evolve: On‑the‑Job Learning for AI Agents Safetensors is Joining the PyTorch Foundation Holo3: Breaking the Computer Use Frontier Any Custom Frontend with Gradio's Backend A New Framework for Evaluating Voice Agents (EVA) Bringing Robotics AI to Embedded Platforms: Dataset Recording, VLA Fine‑Tuning, and On‑Device Optimizations One-Shot Any Web App with Gradio's gr.HTML CUGA on Hugging Face: Democratizing Configurable AI Agents New in llama.cpp: Model Management Building Deep Research: How we Achieved State of the Art OVHcloud on Hugging Face Inference Providers 🔥 20x Faster TRL Fine-tuning with RapidFire AI Building for an Open Future - our new partnership with Google Cloud Aligning to What? Rethinking Agent Generalization in MiniMax M2 Building a Healthcare Robot from Simulation to Deployment with NVIDIA Isaac Sentence Transformers is joining Hugging Face! Unlock the power of images with AI Sheets Supercharge your OCR Pipelines with Open Models Google Cloud C4 Brings a 70% TCO improvement on GPT OSS with Intel and Hugging Face Get your VLM running in 3 simple steps on Intel CPUs Nemotron-Personas-India: Synthesized Data for Sovereign AI Introducing RTEB: A New Standard for Retrieval Evaluation Accelerating Qwen3-8B Agent on Intel® Core™ Ultra with Depth-Pruned Draft Models VibeGame: Exploring Vibe Coding Games Nemotron-Personas-Japan: ソブリン AI のための合成データセット Swift Transformers Reaches 1.0 – and Looks to the Future Smol2Operator: Post-Training GUI Agents for Computer Use SyGra: The One-Stop Framework for Building Data for LLMs and SLMs Gaia2 and ARE: Empowering the community to study agents Scaleway on Hugging Face Inference Providers 🔥 Democratizing AI Safety with RiskRubric.ai Public AI on Hugging Face Inference Providers 🔥 `LeRobotDataset:v3.0`: Bringing large-scale datasets to `lerobot` Visible Watermarking with Gradio Introducing the Palmyra-mini family: Powerful, lightweight, and ready to reason! 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Transformers backend integration in SGLang (LoRA) Fine-Tuning FLUX.1-dev on Consumer Hardware Groq on Hugging Face Inference Providers 🔥 How Long Prompts Block Other Requests - Optimizing LLM Performance Learn the Hugging Face Kernel Hub in 5 Minutes Convert Transformers to ONNX with Hugging Face Optimum Intel and Hugging Face Partner to Democratize Machine Learning Hardware Acceleration Director of Machine Learning Insights [Part 3: Finance Edition] The Annotated Diffusion Model Deep Q-Learning with Space Invaders Graphcore and Hugging Face Launch New Lineup of IPU-Ready Transformers Introducing Pull Requests and Discussions 🥳 Efficient Table Pre-training without Real Data: An Introduction to TAPEX An Introduction to Q-Learning Part 2/2 How Sempre Health is leveraging the Expert Acceleration Program to accelerate their ML roadmap
Bringing serverless GPU inference to Hugging Face users
Philipp Schmid, Jeff Boudier, Rita Kozlov, Nikhil Kothari · 2024-04-02 · via Hugging Face - Blog

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This article is also available in Chinese 简体中文.

Update (November 2024): The integration is no longer available. Please switch to the Hugging Face Inference API, Inference Endpoints, or other deployment options for your AI model needs.

Today, we are thrilled to announce the launch of Deploy on Cloudflare Workers AI, a new integration on the Hugging Face Hub. Deploy on Cloudflare Workers AI makes using open models as a serverless API easy, powered by state-of-the-art GPUs deployed in Cloudflare edge data centers. Starting today, we are integrating some of the most popular open models on Hugging Face into Cloudflare Workers AI, powered by our production solutions, like Text Generation Inference.

With Deploy on Cloudflare Workers AI, developers can build robust Generative AI applications without managing GPU infrastructure and servers and at a very low operating cost: only pay for the compute you use, not for idle capacity.

Generative AI for Developers

This new experience expands upon the strategic partnership we announced last year to simplify the access and deployment of open Generative AI models. One of the main problems developers and organizations face is the scarcity of GPU availability and the fixed costs of deploying servers to start building. Deploy on Cloudflare Workers AI offers an easy, low-cost solution to these challenges, providing serverless access to popular Hugging Face Models with a pay-per-request pricing model.

Let's take a look at a concrete example. Imagine you develop an RAG Application that gets ~1000 requests per day, with an input of 1k tokens and an output of 100 tokens using Meta Llama 2 7B. The LLM inference production costs would amount to about $1 a day.

cloudflare pricing

"We're excited to bring this integration to life so quickly. Putting the power of Cloudflare's global network of serverless GPUs into the hands of developers, paired with the most popular open source models on Hugging Face, will open the doors to lots of exciting innovation by our community around the world," said John Graham-Cumming, CTO, Cloudflare

How it works

Using Hugging Face Models on Cloudflare Workers AI is super easy. Below, you will find step-by-step instructions on how to use Hermes 2 Pro on Mistral 7B, the newest model from Nous Research.

You can find all available models in this Cloudflare Collection.

Note: You need access to a Cloudflare Account and API Token.

You can find the Deploy on Cloudflare option on all available model pages, including models like Llama, Gemma or Mistral.

model card

Open the “Deploy” menu, and select “Cloudflare Workers AI” - this will open an interface that includes instructions on how to use this model and send requests.

Note: If the model you want to use does not have a “Cloudflare Workers AI” option, it is currently not supported. We are working on extending the availability of models together with Cloudflare. You can reach out to us at api-enterprise@huggingface.co with your request.

inference snippet

The integration can currently be used via two options: using the Workers AI REST API or directly in Workers with the Cloudflare AI SDK. Select your preferred option and copy the code into your environment. When using the REST API, you need to make sure the ACCOUNT_ID and API_TOKEN variables are defined.

That’s it! Now you can start sending requests to Hugging Face Models hosted on Cloudflare Workers AI. Make sure to use the correct prompt & template expected by the model.

We’re just getting started

We are excited to collaborate with Cloudflare to make AI more accessible to developers. We will work with the Cloudflare team to make more models and experiences available to you!