How does ZeroGPU work, and is it free?

ZeroGPU provides free, on-demand GPU access for Spaces. Free users get a limited quota; PRO and Team plans get significantly higher quotas and queue priority.

Can I use Hugging Face for commercial projects?

Yes. You can host private models and datasets, deploy on paid Inference Endpoints, and use the Team/Enterprise plans for business needs.

What inference providers are supported, and how does pricing work?

You can access 200k+ models from partners like Nebius, Together AI, and Replicate via a unified API. Pricing is based on partner rates; HF charges no extra service fees.

How do Hugging Face's core libraries (Transformers, Diffusers) integrate with the Hub?

They are tightly integrated. You can load models/datasets directly from Hub IDs using `from_pretrained()`, push trained models with `push_to_hub()`, and leverage the Hub's versioning and metadata.

Directory/Search/Model Hub/Hugging Face

Hugging Face

Hugging Face is the central hub for machine learning collaboration, hosting over 2M models, 500k datasets, and 1M+ applications (Spaces). It provides the open-source libraries (Transformers, Diffusers, Datasets) that form the foundation of modern ML tooling. The platform offers compute via Inference Endpoints and on-demand GPU hardware, with tiered pricing for individuals, teams, and enterprises. Target users include ML researchers, data scientists, AI engineers, and founders building AI-powered products. It serves as a portfolio for work and a place to discover state-of-the-art models across text, image, video, and audio modalities.

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Added on

Sep 22, 2026

ML Model HubDataset RepositorySpaces DemosInference APIGPU ComputeEnterprise SSOOpen Source LibrariesGradio IntegrationCLI & Python Client

Product information

Everything worth knowing about Hugging Face

The complete picture — from everyday features to the technical detail builders and IT folks dig for.

Core features

What it actually does

Model & Dataset Hub

Host and collaborate on over 2 million public models, datasets, and applications with a Git-based workflow.

ML Spaces for Demos

Deploy interactive ML demos and applications directly from the Hub, supporting Gradio, Docker, and custom ZeroGPU hardware.

Inference Providers API

Access 200k+ models from multiple providers through a single, unified API with no service fees.

Dedicated Inference Endpoints

Deploy any ML model on dedicated, autoscaling infrastructure with no cold starts, starting at $0.033/hour.

Team & Enterprise Platform

Advanced collaboration with SSO, audit logs, granular access controls, and dedicated support for organizations.

Technical capabilities

Core ML Library Ecosystem

Open-source stack includes Transformers, Diffusers, Tokenizers, Accelerate, TRL, and other production-grade tools for PyTorch.

Transparent GPU Hardware Pricing

On-demand access to Nvidia T4, L4, L40S, A10G, A100, H100, H200, B200 GPUs and TPUs via Spaces and Endpoints.

Must watch videos

Inference Providers: Best Way to Build with Open Source Models

18:51

Tutorial18:51

Inference Providers: Best Way to Build with Open Source Models

Create your account Today 👉 https://huggingface.short.gy/join Learn how to call open-source AI models through one consistent Hugging Face Inference Providers API: run chat completions with LLMs, generate images, and create embeddings with real Python examples. Choose models and providers from the Hub, reuse OpenAI-compatible clients, compare pricing, and deploy to production—swap models or providers with one line and use free monthly credits with zero Hugging Face markup. --- 🤓 *Topics Covered* - Hugging Face Inference Providers overview: single API for open-source models - Finding models on the Hub with the “Inference available” filter - Creating a fine‑grained Hugging Face access token and enabling “Make calls to inference providers” - Pricing and billing: zero Hugging Face markup; same as calling providers directly - Free monthly credits ($0.10 Free, $2 Pro) and monitoring usage in the Inference Providers dashboard --- ⏰ *Timestamps* 0:00:00 - Intro: What are HF Inference Providers? 0:01:07 - Selecting models & providers on the Hub 0:04:32 - Creating HF access token 0:06:08 - Chat completions with open‑source LLMs 0:10:09 - Text‑to‑image generation 0:12:30 - Embeddings for RAG 0:14:36 - Pricing, credits, and production tips

Why Hugging Face Decided to Combine With Nvidia

7:40

Tutorial7:40

Why Hugging Face Decided to Combine With Nvidia

Hugging Face co-founder Thomas Wolf discusses the impetus for Nvidia's agreement to acquire the artificial intelligence startup in a transaction valued at about $13 billion. He speaks with Ed Ludlow on "Bloomberg Tech." -------- Like this video? Subscribe to Bloomberg Technology on YouTube: https://www.youtube.com/channel/UCrM7B7SL_g1edFOnmj-SDKg Watch the latest full episodes of "Bloomberg Technology" with Caroline Hyde and Ed Ludlow here: https://www.youtube.com/playlist?list=PLfAX25ZLrPGTygCwn55voYZ_LYyKjxokJ Get the latest in tech from Silicon Valley and around the world here: https://www.bloomberg.com/technology Connect with us on... X: https://twitter.com/technology Facebook: https://www.facebook.com/BloombergTechnology Instagram: https://www.instagram.com/bloombergbusiness/ Follow Ed Ludlow on X here: https://twitter.com/EdLudlow Follow Caroline Hyde on X here: https://twitter.com/CarolineHydeTV Listen to the daily Bloomberg Technology podcast here: https://www.bloomberg.com/podcasts/series/bloomberg-technology More from Bloomberg Business Connect with us on... X: https://twitter.com/business Facebook: https://www.facebook.com/bloombergbusiness Instagram: https://www.instagram.com/bloombergbusiness/ LinkedIn: https://www.linkedin.com/company/bloomberg-news/ TikTok: https://www.tiktok.com/@bloombergbusiness

8:19

Tutorial8:19

What Is Hugging Face and How To Use It

🔥 HUGGING FACE TUTORIAL: The Ultimate Open-Source AI Platform for Beginners & Developers 🔥 📞 Book a Call with Me: https://linke.to/apcall 📰 Sign up for my AI newsletter here: https://linke.to/wus20n Discover Hugging Face - the game-changing AI platform with 1.5+ million FREE open-source models that top companies like Google, Meta, and DeepSeek use to release their latest AI technology! In this complete beginner's guide, I'll show you: ✅ What Hugging Face is and why EVERY AI enthusiast needs to know about it ✅ How to instantly use cutting-edge AI applications without coding ✅ Step-by-step process to build your own AI image editing app with Google Gemini ✅ How to download, customize and deploy AI models for FREE ✅ Secret tips to modify open-source code even if you're not a developer Hugging Face is completely FREE to try, with premium features at just $9/month! Stop paying for expensive AI tools when you can access thousands for free. 🔗 Get your Gemini API key: https://aistudio.google.com/app/apikey ⏱️ TIMESTAMPS: 00:28 What is Hugging Face? 01:01 Navigating Hugging Face 04:10 Hands on practice - Make Your Own AI App in Minutes 💬 COMMENT BELOW: Have you used Hugging Face before? What AI projects are you working on? I'd love to check out your spaces! #HuggingFace #AITutorial #MachineLearning #OpenSourceAI #GoogleGemini #AIModelTraining #TechTutorial #AIforBeginners #FreeAITools #AIImageEditing

Who uses it

Real use cases, no hype

Model Hosting & Deployment

Host, version, and deploy pre-trained or fine-tuned models for inference via API or interactive demos.

Dataset Management

Share, version, and explore large datasets with built-in viewers, statistics, and metadata tools.

ML Application Prototyping

Build and share interactive ML demos (Gradio, Docker) on free or paid GPU hardware.

Collaborative Research

Collaborate on models, datasets, and experiments with teams using Git-based workflows.

Production Inference

Deploy scalable, low-latency inference for models using dedicated Endpoints or Inference Providers.

ML Learning & Exploration

Browse state-of-the-art models, read papers, and experiment with state-of-the-art code snippets.

What's great

Massive model hub with 2M+ models
Industry-standard open-source ML libraries (Transformers, Diffusers)
One-click deployment for demos (Spaces) and production (Inference Endpoints)
Thriving community with models, datasets, and Spaces for every modality
Free tier for basic usage and model hosting

Technical strengths

Seamless integration with core HF libraries (Transformers, Tokenizers, Accelerate)
Supports cutting-edge hardware like TPU v5e and H100/H200 GPUs via partners

Where it falls short

Inference costs can add up quickly for large-scale or continuous use
Advanced features (SSO, private dataset viewer, audit logs) are paywalled
Free tier ZeroGPU quotas are low; paid tiers required for priority/throughput
Complex pricing tiers for compute (Spaces, Endpoints) can be confusing
Platform can feel overwhelming due to sheer volume of models/datasets

Technical limitations

Vendor lock-in risk with deep reliance on HF-specific libraries and APIs
Storage egress is included, but high-bandwidth apps may still hit soft limits

For technical folks

The deep-dive specs

FAQ

Includes technical Q&A

PRO is for individuals (boosts storage, inference credits, ZeroGPU quota). Team ($20/user/month) adds organization features like SSO, audit logs, and centralized billing.