{"slug": "hugging-face", "name": "Hugging Face", "category": "infra", "category_name": "Infrastructure and DevOps", "description": "Platform for hosting, sharing and discovering machine learning models, datasets and demos.", "recommenders": 7, "commenters": 7, "warners": 0, "score": 6.575, "rising": 3.02, "first": "2026-03-19", "latest": "2026-09-24", "why": "Valued as the default place to publish and find open models and datasets, with simple distribution and versioning for large files, and as a way to compare inference providers for open models.", "why_is_quote": false, "has_verdict": true, "sentiment": {"recommend": 7, "neutral": 0, "compare": 0, "warn": 0}, "spark": [0, 0, 0, 0, 1, 0, 0, 1, 1, 3, 1, 0], "listed": true, "loved": ["Model publishing hub", "Dataset hosting"], "caveat": "", "short_quote": "One good way to find inference providers for open models is through hugging face", "icon": false, "domain": null, "github_repo": null, "official_url": "https://huggingface.co", "pricing": "freemium", "aliases": ["Hugging Face"], "verdict": [{"text": "Valued as the default place to publish and find open models and datasets", "cites": [{"id": 791379536, "item_id": 49461221, "url": "https://news.ycombinator.com/item?id=49461221", "by": "walrus01", "date": "2026-08-27", "stance": "recommend", "quote": "it's basically become a defacto \"publish your project here\" thing.", "excerpt": "> Forgive me for newb question, but why Hugging Face is a thing at all? Because you can't host arbitrarily large files on github, it's basically become a defacto \"publish your project here\" thing. For a CN domestic equivalent take a look at Modelscope.", "context": "", "use_case": "hosting large model files", "switched_from": "", "story": "Nvidia agrees to acquire Hugging Face for $13B"}, {"id": 778912432, "item_id": 48682027, "url": "https://news.ycombinator.com/item?id=48682027", "by": "drillsteps5", "date": "2026-06-25", "stance": "recommend", "quote": "Huggingface.co is where many of them are published.", "excerpt": "Aside from googling \"how to download and run open weights model\" check out localllama (yes 3Ls) subreddit. Huggingface.co is where many of them are published. There's many providers that run open weights models and give you access. Many decent open weights models cannot be run on consumer-grade hardware (DeepSeek, GLM, many others).", "context": "", "use_case": "finding open weights models", "switched_from": "", "story": "The Unbearable Cheapness of Open Weight Models"}, {"id": 786900592, "item_id": 49181287, "url": "https://news.ycombinator.com/item?id=49181287", "by": "gpugreg", "date": "2026-08-05", "stance": "recommend", "quote": "you can pick some agent trace dataset on https://huggingface.co/datasets?format=format%3Aagent-traces", "excerpt": "To learn about sentiment analysis, I'd look for related datasets and then look at recent code, e.g. here: https://www.kaggle.com/datasets?search=sentiment+analysis For more LLM-specific stuff, you can pick some agent trace dataset on https://huggingface.co/datasets?format=format%3Aagent-traces and check out what people are doing with it (usually linked on the right when you click on a dataset). And of course https://", "context": "", "use_case": "agent trace datasets", "switched_from": "", "story": "DeepSeek V4 Flash on a Single AMD MI300X"}]}, {"text": ", with simple distribution and versioning for large files", "cites": [{"id": 758996514, "item_id": 47437282, "url": "https://news.ycombinator.com/item?id=47437282", "by": "tamnd", "date": "2026-03-19", "stance": "recommend", "quote": "I went with Hugging Face mainly because it is simple and already handles distribution and versioning.", "excerpt": "[Author here] The whole pipeline runs on a single ~$10/month VPS, but it can process hundreds of TB even with just 12GB RAM and a 200GB SSD. The main reason I built this was to have HN data that is easy to query and always up to date, without needing to run your own pipeline first. There are also some interesting ideas in the pipeline, like what I call \"auto-heal\". Happy to share more if anyone is interested :) A lot", "context": "author of HN Parquet archive", "use_case": "dataset hosting", "switched_from": "", "story": "Show HN: Hacker News archive (47M+ items, 11.6GB) as Parquet, updated every 5m"}, {"id": 791379536, "item_id": 49461221, "url": "https://news.ycombinator.com/item?id=49461221", "by": "walrus01", "date": "2026-08-27", "stance": "recommend", "quote": "it's basically become a defacto \"publish your project here\" thing.", "excerpt": "> Forgive me for newb question, but why Hugging Face is a thing at all? Because you can't host arbitrarily large files on github, it's basically become a defacto \"publish your project here\" thing. For a CN domestic equivalent take a look at Modelscope.", "context": "", "use_case": "hosting large model files", "switched_from": "", "story": "Nvidia agrees to acquire Hugging Face for $13B"}]}, {"text": ", and as a way to compare inference providers for open models.", "cites": [{"id": 785964880, "item_id": 49122805, "url": "https://news.ycombinator.com/item?id=49122805", "by": "JamieAlethieum", "date": "2026-07-31", "stance": "recommend", "quote": "One good way to find inference providers for open models is through hugging face", "excerpt": "There are a couple of options. One good way to find inference providers for open models is through hugging face (https://huggingface.co). you can select a model and see which inference providers serve it. you can even access it through hugging face. If you just wanted to test a model or have super light work you can get some free access to alot of open source models through nvidia (https://build.nvidia.com). There is", "context": "", "use_case": "finding LLM inference providers", "switched_from": "", "story": "Ask HN: What are you using for LLM inference in production?"}]}, {"text": " Commenters raise no specific complaints; it suits people downloading open-weights models, publishing datasets, or testing models through hosted inference.", "cites": [{"id": 778912432, "item_id": 48682027, "url": "https://news.ycombinator.com/item?id=48682027", "by": "drillsteps5", "date": "2026-06-25", "stance": "recommend", "quote": "Huggingface.co is where many of them are published.", "excerpt": "Aside from googling \"how to download and run open weights model\" check out localllama (yes 3Ls) subreddit. Huggingface.co is where many of them are published. There's many providers that run open weights models and give you access. Many decent open weights models cannot be run on consumer-grade hardware (DeepSeek, GLM, many others).", "context": "", "use_case": "finding open weights models", "switched_from": "", "story": "The Unbearable Cheapness of Open Weight Models"}, {"id": 758996514, "item_id": 47437282, "url": "https://news.ycombinator.com/item?id=47437282", "by": "tamnd", "date": "2026-03-19", "stance": "recommend", "quote": "I went with Hugging Face mainly because it is simple and already handles distribution and versioning.", "excerpt": "[Author here] The whole pipeline runs on a single ~$10/month VPS, but it can process hundreds of TB even with just 12GB RAM and a 200GB SSD. The main reason I built this was to have HN data that is easy to query and always up to date, without needing to run your own pipeline first. There are also some interesting ideas in the pipeline, like what I call \"auto-heal\". Happy to share more if anyone is interested :) A lot", "context": "author of HN Parquet archive", "use_case": "dataset hosting", "switched_from": "", "story": "Show HN: Hacker News archive (47M+ items, 11.6GB) as Parquet, updated every 5m"}, {"id": 785964880, "item_id": 49122805, "url": "https://news.ycombinator.com/item?id=49122805", "by": "JamieAlethieum", "date": "2026-07-31", "stance": "recommend", "quote": "One good way to find inference providers for open models is through hugging face", "excerpt": "There are a couple of options. One good way to find inference providers for open models is through hugging face (https://huggingface.co). you can select a model and see which inference providers serve it. you can even access it through hugging face. If you just wanted to test a model or have super light work you can get some free access to alot of open source models through nvidia (https://build.nvidia.com). There is", "context": "", "use_case": "finding LLM inference providers", "switched_from": "", "story": "Ask HN: What are you using for LLM inference in production?"}]}], "praise": [{"label": "Model publishing hub", "summary": "Commenters see it as the de facto place where projects and open-weights models get published.", "quotes": [{"id": 791379536, "item_id": 49461221, "url": "https://news.ycombinator.com/item?id=49461221", "by": "walrus01", "date": "2026-08-27", "stance": "recommend", "quote": "it's basically become a defacto \"publish your project here\" thing.", "excerpt": "> Forgive me for newb question, but why Hugging Face is a thing at all? Because you can't host arbitrarily large files on github, it's basically become a defacto \"publish your project here\" thing. For a CN domestic equivalent take a look at Modelscope.", "context": "", "use_case": "hosting large model files", "switched_from": "", "story": "Nvidia agrees to acquire Hugging Face for $13B"}, {"id": 778912432, "item_id": 48682027, "url": "https://news.ycombinator.com/item?id=48682027", "by": "drillsteps5", "date": "2026-06-25", "stance": "recommend", "quote": "Huggingface.co is where many of them are published.", "excerpt": "Aside from googling \"how to download and run open weights model\" check out localllama (yes 3Ls) subreddit. Huggingface.co is where many of them are published. There's many providers that run open weights models and give you access. Many decent open weights models cannot be run on consumer-grade hardware (DeepSeek, GLM, many others).", "context": "", "use_case": "finding open weights models", "switched_from": "", "story": "The Unbearable Cheapness of Open Weight Models"}, {"id": 797237905, "item_id": 49827369, "url": "https://news.ycombinator.com/item?id=49827369", "by": "lemontheme", "date": "2026-09-24", "stance": "recommend", "quote": "if not for arxiv and huggingface, the field of ML would be nowhere near where it is today", "excerpt": "My take: if not for arxiv and huggingface, the field of ML would be nowhere near where it is today. More to your question, I recommend something like semanticscholar to find actual relevant papers. Try to identify researchers that seem trustworthy, then explore the citation network around them. For more hot off the press stuff, follow what gets boosted on social media. Still doesn\u2019t cover the truly niche stuff but it", "context": "", "use_case": "ML models and datasets", "switched_from": "", "story": "ArXiv receives multiyear commitments to support it as an independent nonprofit"}]}, {"label": "Dataset hosting", "summary": "Used to find datasets and to publish them, with distribution and versioning already handled.", "quotes": [{"id": 786900592, "item_id": 49181287, "url": "https://news.ycombinator.com/item?id=49181287", "by": "gpugreg", "date": "2026-08-05", "stance": "recommend", "quote": "you can pick some agent trace dataset on https://huggingface.co/datasets?format=format%3Aagent-traces", "excerpt": "To learn about sentiment analysis, I'd look for related datasets and then look at recent code, e.g. here: https://www.kaggle.com/datasets?search=sentiment+analysis For more LLM-specific stuff, you can pick some agent trace dataset on https://huggingface.co/datasets?format=format%3Aagent-traces and check out what people are doing with it (usually linked on the right when you click on a dataset). And of course https://", "context": "", "use_case": "agent trace datasets", "switched_from": "", "story": "DeepSeek V4 Flash on a Single AMD MI300X"}, {"id": 758996514, "item_id": 47437282, "url": "https://news.ycombinator.com/item?id=47437282", "by": "tamnd", "date": "2026-03-19", "stance": "recommend", "quote": "I went with Hugging Face mainly because it is simple and already handles distribution and versioning.", "excerpt": "[Author here] The whole pipeline runs on a single ~$10/month VPS, but it can process hundreds of TB even with just 12GB RAM and a 200GB SSD. The main reason I built this was to have HN data that is easy to query and always up to date, without needing to run your own pipeline first. There are also some interesting ideas in the pipeline, like what I call \"auto-heal\". Happy to share more if anyone is interested :) A lot", "context": "author of HN Parquet archive", "use_case": "dataset hosting", "switched_from": "", "story": "Show HN: Hacker News archive (47M+ items, 11.6GB) as Parquet, updated every 5m"}]}, {"label": "Inference provider comparison", "summary": "Lets you pick a model, see which inference providers serve it, and access it directly.", "quotes": [{"id": 785964880, "item_id": 49122805, "url": "https://news.ycombinator.com/item?id=49122805", "by": "JamieAlethieum", "date": "2026-07-31", "stance": "recommend", "quote": "One good way to find inference providers for open models is through hugging face", "excerpt": "There are a couple of options. One good way to find inference providers for open models is through hugging face (https://huggingface.co). you can select a model and see which inference providers serve it. you can even access it through hugging face. If you just wanted to test a model or have super light work you can get some free access to alot of open source models through nvidia (https://build.nvidia.com). There is", "context": "", "use_case": "finding LLM inference providers", "switched_from": "", "story": "Ask HN: What are you using for LLM inference in production?"}]}, {"label": "Large file hosting", "summary": "Hosts files too large for GitHub.", "quotes": [{"id": 791379536, "item_id": 49461221, "url": "https://news.ycombinator.com/item?id=49461221", "by": "walrus01", "date": "2026-08-27", "stance": "recommend", "quote": "it's basically become a defacto \"publish your project here\" thing.", "excerpt": "> Forgive me for newb question, but why Hugging Face is a thing at all? Because you can't host arbitrarily large files on github, it's basically become a defacto \"publish your project here\" thing. For a CN domestic equivalent take a look at Modelscope.", "context": "", "use_case": "hosting large model files", "switched_from": "", "story": "Nvidia agrees to acquire Hugging Face for $13B"}]}, {"label": "Everyday tool", "summary": "Some commenters use it all the time and credit it with advancing the ML field.", "quotes": [{"id": 791362400, "item_id": 49460150, "url": "https://news.ycombinator.com/item?id=49460150", "by": "xkbarkar", "date": "2026-08-27", "stance": "recommend", "quote": "I use huggingface all the time.", "excerpt": "Oh no. I use huggingface all the time. Sad news.", "context": "", "use_case": "", "switched_from": "", "story": "Nvidia agrees to acquire Hugging Face for $13B"}, {"id": 797237905, "item_id": 49827369, "url": "https://news.ycombinator.com/item?id=49827369", "by": "lemontheme", "date": "2026-09-24", "stance": "recommend", "quote": "if not for arxiv and huggingface, the field of ML would be nowhere near where it is today", "excerpt": "My take: if not for arxiv and huggingface, the field of ML would be nowhere near where it is today. More to your question, I recommend something like semanticscholar to find actual relevant papers. Try to identify researchers that seem trustworthy, then explore the citation network around them. For more hot off the press stuff, follow what gets boosted on social media. Still doesn\u2019t cover the truly niche stuff but it", "context": "", "use_case": "ML models and datasets", "switched_from": "", "story": "ArXiv receives multiyear commitments to support it as an independent nonprofit"}]}], "caveats": [], "who": [{"by": "tamnd", "context": "author of HN Parquet archive", "url": "https://news.ycombinator.com/item?id=47437282", "date": "2026-03-19"}], "tags": ["for ML researchers", "for open-weights models", "for publishing datasets"], "use_cases": ["ml models and datasets", "hosting large model files", "agent trace datasets", "finding llm inference providers", "finding open weights models", "dataset hosting"], "compared": [], "makers": [], "threads": [{"url": "https://news.ycombinator.com/item?id=49458161", "title": "Nvidia agrees to acquire Hugging Face for $13B", "mentions": 2}, {"url": "https://news.ycombinator.com/item?id=49823664", "title": "ArXiv receives multiyear commitments to support it as an independent nonprofit", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=49166386", "title": "DeepSeek V4 Flash on a Single AMD MI300X", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=49121047", "title": "Ask HN: What are you using for LLM inference in production?", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=48668255", "title": "The Unbearable Cheapness of Open Weight Models", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=47378781", "title": "Show HN: Hacker News archive (47M+ items, 11.6GB) as Parquet, updated every 5m", "mentions": 1}], "sentiment_by_month": {"2025-11": {}, "2025-12": {}, "2026-01": {}, "2026-02": {}, "2026-03": {"recommend": 1}, "2026-04": {}, "2026-05": {}, "2026-06": {"recommend": 1}, "2026-07": {"recommend": 1}, "2026-08": {"recommend": 3}, "2026-09": {"recommend": 1}, "2026-10": {}}, "quotes": [{"id": 797237905, "item_id": 49827369, "url": "https://news.ycombinator.com/item?id=49827369", "by": "lemontheme", "date": "2026-09-24", "stance": "recommend", "quote": "if not for arxiv and huggingface, the field of ML would be nowhere near where it is today", "excerpt": "My take: if not for arxiv and huggingface, the field of ML would be nowhere near where it is today. More to your question, I recommend something like semanticscholar to find actual relevant papers. Try to identify researchers that seem trustworthy, then explore the citation network around them. For more hot off the press stuff, follow what gets boosted on social media. Still doesn\u2019t cover the truly niche stuff but it", "context": "", "use_case": "ML models and datasets", "switched_from": "", "story": "ArXiv receives multiyear commitments to support it as an independent nonprofit"}, {"id": 791379536, "item_id": 49461221, "url": "https://news.ycombinator.com/item?id=49461221", "by": "walrus01", "date": "2026-08-27", "stance": "recommend", "quote": "it's basically become a defacto \"publish your project here\" thing.", "excerpt": "> Forgive me for newb question, but why Hugging Face is a thing at all? Because you can't host arbitrarily large files on github, it's basically become a defacto \"publish your project here\" thing. For a CN domestic equivalent take a look at Modelscope.", "context": "", "use_case": "hosting large model files", "switched_from": "", "story": "Nvidia agrees to acquire Hugging Face for $13B"}, {"id": 791362400, "item_id": 49460150, "url": "https://news.ycombinator.com/item?id=49460150", "by": "xkbarkar", "date": "2026-08-27", "stance": "recommend", "quote": "I use huggingface all the time.", "excerpt": "Oh no. I use huggingface all the time. Sad news.", "context": "", "use_case": "", "switched_from": "", "story": "Nvidia agrees to acquire Hugging Face for $13B"}, {"id": 786900592, "item_id": 49181287, "url": "https://news.ycombinator.com/item?id=49181287", "by": "gpugreg", "date": "2026-08-05", "stance": "recommend", "quote": "you can pick some agent trace dataset on https://huggingface.co/datasets?format=format%3Aagent-traces", "excerpt": "To learn about sentiment analysis, I'd look for related datasets and then look at recent code, e.g. here: https://www.kaggle.com/datasets?search=sentiment+analysis For more LLM-specific stuff, you can pick some agent trace dataset on https://huggingface.co/datasets?format=format%3Aagent-traces and check out what people are doing with it (usually linked on the right when you click on a dataset). And of course https://", "context": "", "use_case": "agent trace datasets", "switched_from": "", "story": "DeepSeek V4 Flash on a Single AMD MI300X"}, {"id": 785964880, "item_id": 49122805, "url": "https://news.ycombinator.com/item?id=49122805", "by": "JamieAlethieum", "date": "2026-07-31", "stance": "recommend", "quote": "One good way to find inference providers for open models is through hugging face", "excerpt": "There are a couple of options. One good way to find inference providers for open models is through hugging face (https://huggingface.co). you can select a model and see which inference providers serve it. you can even access it through hugging face. If you just wanted to test a model or have super light work you can get some free access to alot of open source models through nvidia (https://build.nvidia.com). There is", "context": "", "use_case": "finding LLM inference providers", "switched_from": "", "story": "Ask HN: What are you using for LLM inference in production?"}, {"id": 778912432, "item_id": 48682027, "url": "https://news.ycombinator.com/item?id=48682027", "by": "drillsteps5", "date": "2026-06-25", "stance": "recommend", "quote": "Huggingface.co is where many of them are published.", "excerpt": "Aside from googling \"how to download and run open weights model\" check out localllama (yes 3Ls) subreddit. Huggingface.co is where many of them are published. There's many providers that run open weights models and give you access. Many decent open weights models cannot be run on consumer-grade hardware (DeepSeek, GLM, many others).", "context": "", "use_case": "finding open weights models", "switched_from": "", "story": "The Unbearable Cheapness of Open Weight Models"}, {"id": 758996514, "item_id": 47437282, "url": "https://news.ycombinator.com/item?id=47437282", "by": "tamnd", "date": "2026-03-19", "stance": "recommend", "quote": "I went with Hugging Face mainly because it is simple and already handles distribution and versioning.", "excerpt": "[Author here] The whole pipeline runs on a single ~$10/month VPS, but it can process hundreds of TB even with just 12GB RAM and a 200GB SSD. The main reason I built this was to have HN data that is easy to query and always up to date, without needing to run your own pipeline first. There are also some interesting ideas in the pipeline, like what I call \"auto-heal\". Happy to share more if anyone is interested :) A lot", "context": "author of HN Parquet archive", "use_case": "dataset hosting", "switched_from": "", "story": "Show HN: Hacker News archive (47M+ items, 11.6GB) as Parquet, updated every 5m"}]}