# inference-providers Hugging Face Inference Providers is a developer-facing platform that gives access to machine-learning models through multiple inference providers and consistent APIs and SDKs. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How to Move an AI App from a Frontier Model to Open Weights DevFeed: [How to Move an AI App from a Frontier Model to Open Weights]() Original publisher: [Read original article]() Author: Gus Schissler Published: 2026-09-10T12:00:43Z Content type: tutorial Language: en Sources: [Atomic Object]() Topics: [Artificial Intelligence](), [Large Language Model](), [Benchmark](), [inference-providers](), [codex]() Tags: [ai](), [ai-for-developers](), [benchmark](), [codex](), [how-to](), [inference](), [inference-providers](), [llm](), [prototyping]() ### AI overview This tutorial describes moving an AI document-processing prototype from a frontier model to cheaper open-weight models through hosted inference providers. It explains how to define a trusted gold set from real project outputs and use workflow-specific evaluation to identify where lower-cost models fail. ### Source excerpt At Atomic Object, we're encouraged to prototype and dogfood internal projects. Over the last four months, I've been building a prototype to help solve a part of my job that I dislike. The first version used an LLM to extract and classify information from documents, then connect related pieces so humans and agents could retrieve [...] The post How to Move an AI App from a Frontier Model to Open Weights appeared first on Atomic Spin. ## NVIDIA to Acquire Hugging Face for $12.93B, Pledges the Platform Stays Open and Hardware Neutral DevFeed: [NVIDIA to Acquire Hugging Face for $12.93B, Pledges the Platform Stays Open and Hardware Neutral]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-04T18:01:12Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [hugging face](), [Nvidia](), [AI Development](), [AI Infrastructure](), [Application Development](), [datasets](), [Cloud](), [Deployment](), [model-deployment](), [inference-providers]() Tags: [ai](), [ai-infrastructure](), [application-development](), [cloud](), [creators](), [datasets](), [deployment](), [enterprise](), [hugging-face](), [inference-providers](), [models](), [multi-cloud](), [nvidia](), [platforms]() ### AI overview NVIDIA has agreed to acquire Hugging Face for $12.93 billion, with plans to expand its infrastructure and AI development capabilities. Hugging Face is expected to retain its brand and operate as an open, hardware-neutral platform supporting models, datasets, applications, multiple clouds, accelerators, and inference providers. ### Source excerpt NVIDIA has agreed to acquire Hugging Face for $12.93 billion, a transaction that would extend the company's position from accelerated compute and AI infrastructure into one of the industry's most widely used platforms for open models, datasets, and application development. In an announcement published on the NVIDIA website, CEO Jensen Huang said the company plans The post NVIDIA to Acquire Hugging Face for $12.93B, Pledges the Platform Stays Open and Hardware Neutral appeared first on StorageReview.com. ## Access and share AI Gateway leaderboard data DevFeed: [Access and share AI Gateway leaderboard data]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-07-14T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Artificial Intelligence](), [API](), [CSV](), [inference-providers](), [data]() Tags: [ai](), [ai-gateway](), [api](), [data](), [images](), [inference-providers](), [leaderboard](), [metrics](), [model](), [open](), [ranking](), [time](), [tokens](), [videos]() ### AI overview Vercel has opened the data behind the AI Gateway leaderboards under the CC BY 4.0 license. The data can be downloaded as CSV or queried through an export API, while charts can be shared as branded PNG images. The leaderboards track daily production usage across models, labs, apps, and inference providers. ### Source excerpt We are making the data behind the AI Gateway leaderboards open under the CC BY 4.0 license. You can now download or query the data through the leaderboard-export API endpoint and render any chart as a shareable image. The AI Gateway leaderboards show how AI is used in production, ranking traffic for models, labs, apps, and providers. Data is aggregated daily across trillions of tokens, so you can see what gets adopted and how that changes over time. For deeper analysis, see the July AI Gateway Production Index. What's ranked There are four leaderboards, each with its own metrics: Leaderboard Ranks Metrics Models Individual models Requests, token volume, spend, images or videos generated Labs Model labs Requests, token volume, spend, images or videos generated Apps Opted-in apps built on the AI Gateway Token volume, spend Providers Inference providers Token volume, spend Models and labs can be filtered by modality (text, image, video) and show a daily percentage share over time; apps and providers are aggregated across all modalities and show a ranked top list. Open data The data behind the leaderboards is open, published under Creative Commons Attribution 4.0 (CC BY 4.0). You are free to use, share, and adapt it, including commercially, as long as you give credit, link to the license, and indicate if changes were made. Every chart and ranked list has a download button that exports the current view as a CSV. For programmatic access, use the export endpoint, which returns the same data and is cached for 24 hours: For models and labs, each row is one entity's daily share of a single metric. One response includes rows for requests, tokens, spend, imageCount, and videoCount, so filter on the metric field to pull out the series you want. Share a chart Every chart has a share button that turns the current view into an image. Pick an aspect ratio (landscape, square, or portrait), then download it as a PNG or copy it to your clipboard. The image includes the legend, title, a ## DeepInfra on Hugging Face Inference Providers 🔥 DevFeed: [DeepInfra on Hugging Face Inference Providers 🔥]() Original publisher: [Read original article]() Author: Aray Sultanbekova; Shang-Pin; Utemuratov; Yessen K; Oguz Vuruskaner; Célina Hanouti; Simon Brandeis; Lucain Pouget Published: 2026-04-29T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [deepinfra](), [hugging face](), [inference-providers](), [AI Inference](), [SDKs](), [Large Language Model](), [API keys](), [text-generation](), [text-to-image]() Tags: [ai-inference](), [api](), [api-keys](), [applications](), [artificial-intelligence](), [code](), [deepinfra](), [enterprise](), [hub](), [hugging-face](), [inference](), [inference-providers](), [javascript](), [llms](), [open](), [open-source](), [partnerships](), [pricing](), [python](), [sdks](), [text-generation](), [text-to-image]() ### AI overview DeepInfra is now a supported Inference Provider on the Hugging Face Hub, with serverless access to more than 100 models and integration with Hugging Face's JavaScript and Python SDKs. The article describes provider selection, API-key and routed-by-Hugging-Face modes, supported model tasks, and initial access to conversational and text-generation models. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Liberate your OpenClaw DevFeed: [Liberate your OpenClaw]() Original publisher: [Read original article]() Author: Clem 🤗; ben burtenshaw; Pedro Cuenca; Jeff Boudier; merve; Niels Rogge; Victor Mustar; Mishig ᠮᠢᠰᠾᠢᠭ Published: 2026-03-27T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [OpenClaw](), [Open Source](), [hugging face](), [inference-providers](), [llama.cpp]() Tags: [agents](), [guide](), [hugging-face](), [inference-providers](), [llama-cpp](), [local-ai](), [open-source](), [openclaw](), [privacy](), [server]() ### AI overview This tutorial explains how to restore OpenClaw agents using open models through Hugging Face Inference Providers or by running models locally with llama.cpp. It compares hosted and local approaches, covering privacy, cost, hardware, model selection, configuration, and local server setup. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Open Responses: What you need to know DevFeed: [Open Responses: What you need to know]() Original publisher: [Read original article]() Author: shaun smith; ben burtenshaw; merve; Pedro Cuenca Published: 2026-01-15T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [open-responses](), [API](), [Open Source](), [inference-providers](), [Streaming](), [JSON](), [OpenAI]() Tags: [agentic](), [agents](), [api](), [apis](), [autonomous](), [community](), [inference](), [inference-providers](), [json](), [mcp](), [open-responses](), [open-source](), [openai](), [responses](), [streaming](), [tool](), [workflow]() ### AI overview Open Responses is presented as an open inference standard for agentic workloads. The article explains how it extends and open-sources the Responses API, supports interoperable routing across inference providers, and introduces standardized configuration, semantic streaming events, extensibility, and optional reasoning fields. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## OVHcloud on Hugging Face Inference Providers 🔥 DevFeed: [OVHcloud on Hugging Face Inference Providers 🔥]() Original publisher: [Read original article]() Author: Gilles Closset; Fabien Ric; Elias Tourneux Published: 2025-11-24T16:08:47Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [hugging face](), [inference-providers](), [Serverless](), [SDKs](), [AI Models](), [API]() Tags: [ai-models](), [api](), [hub](), [hugging-face](), [inference-providers](), [latency](), [multimodal](), [production](), [sdks](), [serverless](), [text-generation](), [tokens]() ### AI overview Hugging Face announces OVHcloud as a supported Inference Provider on the Hugging Face Hub. The serverless service provides managed access to open-weight and frontier AI models through API calls, with SDK integration, European infrastructure, pay-per-token pricing, low latency, multimodal capabilities, structured outputs, function calling, text generation, and embeddings. ### Source excerpt We're thrilled to share that OVHcloud is now a supported Inference Provider on the Hugging Face Hub! OVHcloud joins our growing ecosystem, enhancing the breadth and capabilities of serverless inference directly on the Hub's model pages. Inference Providers are also seamlessly integrated into our client SDKs (for both JS and Python), making it super easy to use a wide variety of models with your preferred providers. ## Unlock the power of images with AI Sheets DevFeed: [Unlock the power of images with AI Sheets]() Original publisher: [Read original article]() Author: Ame Vi; Daniel Vila; Francisco Aranda; Damián Pumar; Leandro von Werra; Thomas Wolf Published: 2025-10-21T00:00:00Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [hugging face](), [AI Development](), [AI Models](), [inference-providers](), [datasets](), [Open Source](), [CSV](), [parquet]() Tags: [ai](), [ai-models](), [code](), [datasets](), [hugging-face](), [inference-providers](), [open-source](), [parquet](), [release](), [vision]() ### AI overview Hugging Face has released a major update to AI Sheets, an open-source spreadsheet tool that uses AI models to build, transform, and enrich datasets without code. The update adds vision support for analyzing images, extracting structured information, generating visuals, and editing images, with access to thousands of open models through Inference Providers. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Scaleway on Hugging Face Inference Providers 🔥 DevFeed: [Scaleway on Hugging Face Inference Providers 🔥]() Original publisher: [Read original article]() Author: Guillaume Noale; Franck Pagny; Fred Bardolle; Guillaume Calmettes; Constance Morales; Célina Hanouti; Julien Chaumond; Simon Brandeis; Lucain Pouget Published: 2025-09-19T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [hugging face](), [scaleway](), [Inference](), [inference-providers](), [Serverless](), [gpt-oss](), [SDKs](), [API keys](), [Latency](), [Sovereign AI]() Tags: [api-keys](), [enterprise](), [function-calling](), [gemma](), [gpt-oss](), [hub](), [hugging-face](), [inference](), [inference-providers](), [llms](), [low-latency](), [multimodal](), [open](), [open-source](), [partnerships](), [providers](), [qwen3](), [scaleway](), [sdks](), [serverless](), [text-generation]() ### AI overview Hugging Face announces Scaleway as a supported Inference Provider on the Hugging Face Hub. The integration provides serverless access to open-weight and frontier AI models through model pages, client SDKs, and APIs, with European data centers, pay-per-token pricing, low latency, and production-oriented features. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Public AI on Hugging Face Inference Providers 🔥 DevFeed: [Public AI on Hugging Face Inference Providers 🔥]() Original publisher: [Read original article]() Author: Joseph Low; Joshua Tan; Célina Hanouti; Julien Chaumond; Simon Brandeis; Lucain Pouget Published: 2025-09-17T00:00:00Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [inference-providers](), [hugging face](), [Inference](), [Open Source]() Tags: [ai-inference](), [api](), [enterprise](), [hub](), [hugging-face](), [inference](), [inference-providers](), [llms](), [open-source](), [partnerships](), [public-ai]() ### AI overview Hugging Face announces Public AI as a supported Inference Provider on the Hub. Public AI offers access to public and sovereign models through distributed vLLM-based infrastructure and OpenAI-compatible APIs. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Welcome GPT OSS, the new open-source model family from OpenAI! DevFeed: [Welcome GPT OSS, the new open-source model family from OpenAI!]() Original publisher: [Read original article]() Author: Vaibhav Srivastav; Pedro Cuenca; Lewis Tunstall; Clem 🤗; Matthew Carrigan; Clémentine Fourrier; Célina Hanouti; Lucain Pouget; Marc Sun; Simon Pagezy Published: 2025-08-05T00:00:00Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [gpt-oss](), [Open Source](), [OpenAI](), [Artificial Intelligence](), [inference-providers](), [llama.cpp](), [moe](), [Ollama](), [Python](), [quantization](), [Transformers](), [vllm]() Tags: [api](), [artificial-intelligence](), [community](), [gpt](), [gpt-oss](), [inference-providers](), [javascript](), [llama-cpp](), [llm](), [moe](), [ollama](), [open-source](), [openai](), [python](), [quantization](), [transformers](), [vllm]() ### AI overview Hugging Face welcomes OpenAI's gpt-oss open-source model family. The article describes the models' Apache 2.0 licensing, local deployment options, reasoning and tool-use capabilities, MoE architecture, quantization, supported inference implementations, and access through Inference Providers and the Responses API. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Groq on Hugging Face Inference Providers 🔥 DevFeed: [Groq on Hugging Face Inference Providers 🔥]() Original publisher: [Read original article]() Author: Ben Ankiel; Hatice Ozen; Célina Hanouti; Lucain Pouget; Simon Brandeis Published: 2025-06-16T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [groq](), [inference-providers](), [hugging face](), [Inference](), [model-deployment](), [Open Source](), [SDKs](), [API]() Tags: [api](), [api-keys](), [artificial-intelligence](), [enterprise](), [groq](), [hub](), [hugging-face](), [inference](), [inference-providers](), [large-language-models-llms](), [latency](), [llms](), [open-source](), [partnerships](), [real-time](), [sdks]() ### AI overview Groq is now available as an Inference Provider on the Hugging Face Hub, including model pages and Hugging Face client SDKs for JavaScript and Python. The article describes Groq's LPU technology, its low-latency and high-throughput inference for LLMs, support for open models such as Meta's Llama 4 and Qwen's QWQ-32B, API access, and custom-key or Hugging Face-routed usage options. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Featherless AI on Hugging Face Inference Providers 🔥 DevFeed: [Featherless AI on Hugging Face Inference Providers 🔥]() Original publisher: [Read original article]() Author: Wesley George; Poh Nean; Eugene Cheah; Célina Hanouti; Lucain Pouget; Simon Brandeis Published: 2025-06-12T00:00:00Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [inference-providers](), [hugging face](), [AI Inference](), [SDKs](), [Serverless](), [API keys](), [Orchestration]() Tags: [ai-inference](), [api-keys](), [enterprise](), [featherless](), [hub](), [hugging-face](), [inference-providers](), [llms](), [orchestration](), [partnerships](), [recursal](), [sdks](), [serverless]() ### AI overview Hugging Face announces Featherless AI as a supported Inference Provider on the Hugging Face Hub. The article explains its serverless inference offering, provider routing options, API key handling, and use through the Hugging Face client SDKs. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Cohere on Hugging Face Inference Providers 🔥 DevFeed: [Cohere on Hugging Face Inference Providers 🔥]() Original publisher: [Read original article]() Author: Vaibhav Srivastav; ben burtenshaw; merve; Célina Hanouti; Alejandro Rodriguez; Julien Chaumond; Simon Brandeis Published: 2025-04-16T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [cohere](), [inference-providers](), [hugging face](), [Retrieval Augmented Generation (RAG)](), [multimodal](), [Open Source]() Tags: [artificial-intelligence](), [cohere](), [embeddings](), [enterprise](), [generative-ai](), [hub](), [hugging-face](), [inference](), [inference-providers](), [llms](), [multimodal](), [open](), [partnerships](), [rag](), [ranking]() ### AI overview Cohere is now supported as an Inference Provider on the Hugging Face Hub, allowing serverless inference for a selection of Cohere and Cohere Labs models. The article highlights models for enterprise AI, multilingual applications, retrieval-augmented generation, tool use, low-cost or low-latency workloads, and vision-language tasks. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Introducing Three New Serverless Inference Providers: Hyperbolic, Nebius AI Studio, and Novita 🔥 DevFeed: [Introducing Three New Serverless Inference Providers: Hyperbolic, Nebius AI Studio, and Novita 🔥]() Original publisher: [Read original article]() Author: Julien Chaumond; Bertrand Chevrier; Vaibhav Srivastav; Simon Brandeis; Albert Abdulmanov; Viktor Hu; Connor Chevli Published: 2025-02-18T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [inference-providers](), [hugging face](), [model-deployment](), [API keys](), [Artificial Intelligence]() Tags: [announcement](), [api](), [api-keys](), [hub](), [hugging-face](), [image](), [inference](), [inference-providers]() ### AI overview Hugging Face introduces three new serverless inference providers: Hyperbolic, Nebius AI Studio, and Novita. The providers add access to models such as DeepSeek-R1 and Flux.1, with support for custom provider keys or routing through Hugging Face. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Welcome to Inference Providers on the Hub 🔥 DevFeed: [Welcome to Inference Providers on the Hub 🔥]() Original publisher: [Read original article]() Author: Burkay Gur; Zeke Sikelianos; Anton McGonnell; Hassan El Mghari; Simon Brandeis; Bertrand Chevrier; Julien Chaumond Published: 2025-01-28T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [inference-providers](), [hugging face](), [API keys](), [Open Source Models & Datasets]() Tags: [announcement](), [api-keys](), [hub](), [hugging-face](), [inference-providers](), [open-source](), [serverless](), [token]() ### AI overview Hugging Face introduces Inference Providers on the Hub, offering unified access to serverless model inference through partner providers. Users can configure provider API keys and preferences, while requests may either go directly to a provider or be routed through Hugging Face. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science.