Hugging Face is the collaboration platform for the machine learning community. The Hugging Face Hub works as a central place where anyone can share, explore, discover, and experiment with open-source ML. HF empowers the next generation of machine learning engineers, scientists, and end users to learn, collaborate and share their work to build an open and ethical AI future together. With the fast-growing community, some of the most used open-source ML libraries and tools, and a talented science team exploring the edge of tech, Hugging Face is at the heart of the AI revolution.
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Related Coverage
AI
NVIDIA’s Reported Hugging Face Deal Would Put the AI Model Commons Inside the GPU Stack
NVIDIA's reported $12.9 billion agreement to acquire Hugging Face is not just another AI infrastructure deal. If completed, it would put one of the industry's most important open model hubs inside the company that already defines much of the accelerated computing stack.
AI
NVIDIA Rubin, Together AI’s $800M Bet, and Hugging Face’s Native vLLM Speed: The Infrastructure Convergence Reshaping AI
This week, NVIDIA unveiled the Rubin GPU architecture purpose-built for agentic AI, Together AI raised $800M to scale open-source inference, and Hugging Face eliminated the vLLM porting bottleneck. Here's what the convergence means for production AI infrastructure.
Agentic AI
Agentic AI This Week: Hugging Face’s New Benchmark, Cohere’s Open Coding Model, and a Cross-Industry Discovery Protocol
Hugging Face launches a new agent benchmark and discovery protocol, Cohere open-sources its first agentic coding model, IBM Research shows why structured reasoning beats raw LLM power, and Google bets the platform on agent-first development.
AI
Agentic AI Infrastructure: How NVIDIA, vLLM, and Hugging Face Are Rebuilding Inference for the Agent Era
From session-aware KV cache orchestration to agent-optimized CLIs, the infrastructure layer is racing to support long-running AI agents. NVIDIA Dynamo 1.0 enters production, vLLM and Ollama ship agent-relevant updates, and Hugging Face rebuilds its CLI for machine consumers.
AI
Robotics VLAs on embedded chips: NXP and Hugging Face outline the real bottleneck (and it’s not just compression)
A Hugging Face post with NXP argues that deploying vision-language-action (VLA) models on embedded robots is a systems engineering problem: dataset quality, pipeline decomposition, latency-aware scheduling, and asynchronous inference matter as much as quantization.
