DualPath: Breaking the Storage Bandwidth Bottleneck in Agentic LLM Inference
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victor
submitted a paper to Daily Papers 1 day ago
Post
930
Interesting article: use Claude Code to help open models write CUDA kernels (for eg) by turning CC traces into Skills. They made a library out of it 👀
https://huggingface.co/blog/upskill
https://huggingface.co/blog/upskill
Post
3533
👉 What happened in AI in 2025? 👈
We prepared the 2025 version of the HF AI Timeline Grid, highlighting open vs API-based model releases, and allowing you to browse and filter by access, modality, and release type!
Play with it here:
2025-ai-timeline/2025-ai-timeline
Here's my personal quarterly TL;DR:
1️⃣ Q1 — Learning to Reason
Deepseek not only releases a top-notch reasoning model, but shows how to train them and compete with closed frontier models. OpenAI debuts Deep Research.
Significant milestones: DeepSeek R1 & R1-Zero, Qwen 2.5 VL, OpenAI Deep Research, Gemini 2.5 Pro (experimental)
2️⃣ Q2 — Multimodality and Coding
More LLMs embrace multimodality by default, and there's a surge in coding agents. Strong vision, audio, and generative models emerge.
Significant milestones: Llama 4, Qwen 3, Imagen 4, OpenAI Codex, Google Jules, Claude 4
3️⃣ Q3 — "Gold" rush, OpenAI opens up, the community goes bananas
Flagship models get gold in Math olympiads and hard benchmarks. OpenAI releases strong open source models and Google releases the much anticipated nano-banana for image generation and editing. Agentic workflows become commonplace.
Significant milestones: Gemini and OpenAI IMO Gold, gpt-oss, Gemini 2.5 Flash Image, Grok 4, Claude Sonnet 4.5
4️⃣ Q4 — Mistral returns, leaderboard hill-climbing
Mistral is back with updated model families. All labs release impressive models to wrap up the year!
Significant milestones: Claude Opus 4.5, DeepSeek Math V2, FLUX 2, GPT 5.1, Kimi K2 Thinking, Nano Banana Pro, GLM 4.7, Gemini 3, Mistral 3, MiniMax M2.1 🤯
Credits
🙏 NHLOCAL for the source data https://github.com/NHLOCAL/AiTimeline
🫡 @reach-vb for the original idea, design and recipe
🙌 @ariG23498 and yours truly for compiling and verifying the 2025 edition
🥳 Here's to 2026, wishing it becomes the best year ever for open releases and on-device-first use-cases! 🥂
We prepared the 2025 version of the HF AI Timeline Grid, highlighting open vs API-based model releases, and allowing you to browse and filter by access, modality, and release type!
Play with it here:
2025-ai-timeline/2025-ai-timeline
Here's my personal quarterly TL;DR:
1️⃣ Q1 — Learning to Reason
Deepseek not only releases a top-notch reasoning model, but shows how to train them and compete with closed frontier models. OpenAI debuts Deep Research.
Significant milestones: DeepSeek R1 & R1-Zero, Qwen 2.5 VL, OpenAI Deep Research, Gemini 2.5 Pro (experimental)
2️⃣ Q2 — Multimodality and Coding
More LLMs embrace multimodality by default, and there's a surge in coding agents. Strong vision, audio, and generative models emerge.
Significant milestones: Llama 4, Qwen 3, Imagen 4, OpenAI Codex, Google Jules, Claude 4
3️⃣ Q3 — "Gold" rush, OpenAI opens up, the community goes bananas
Flagship models get gold in Math olympiads and hard benchmarks. OpenAI releases strong open source models and Google releases the much anticipated nano-banana for image generation and editing. Agentic workflows become commonplace.
Significant milestones: Gemini and OpenAI IMO Gold, gpt-oss, Gemini 2.5 Flash Image, Grok 4, Claude Sonnet 4.5
4️⃣ Q4 — Mistral returns, leaderboard hill-climbing
Mistral is back with updated model families. All labs release impressive models to wrap up the year!
Significant milestones: Claude Opus 4.5, DeepSeek Math V2, FLUX 2, GPT 5.1, Kimi K2 Thinking, Nano Banana Pro, GLM 4.7, Gemini 3, Mistral 3, MiniMax M2.1 🤯
Credits
🙏 NHLOCAL for the source data https://github.com/NHLOCAL/AiTimeline
🫡 @reach-vb for the original idea, design and recipe
🙌 @ariG23498 and yours truly for compiling and verifying the 2025 edition
🥳 Here's to 2026, wishing it becomes the best year ever for open releases and on-device-first use-cases! 🥂
- 2 replies
Post
3434
Nvidia is on a roll lately. Nemotron 3 Nano is my new fav local model, but here's the real flex: they published the entire evaluation setup. Configs, prompts, logs, all of it. This is how you do open models 🔥
https://huggingface.co/blog/nvidia/nemotron-3-nano-evaluation-recipe
https://huggingface.co/blog/nvidia/nemotron-3-nano-evaluation-recipe
reach-vb
authored a paper 3 months ago
Post
2913
🚀 Big news for AI builders!
We’re thrilled to announce that the Qwen3-VL family of vision-language models is now available on Azure AI Foundry, thanks to our collaboration with Microsoft.
We bring open-source innovation to enterprise-grade AI infrastructure, making it easier than ever for enterprise to deploy and scale the latest and greatest from models from hugging Face securely within Azure.
🔍 Highlights:
- Deploy Qwen3-VL instantly via managed endpoints
- Built-in governance, telemetry, and lifecycle management
- True multimodal reasoning — vision, language, and code understanding
- State-of-the-art performance, outperforming closed-source models like Gemini 2.5 Pro and GPT-5
- Available in both *Instruct* and *Thinking* modes, across 24 model sizes
👉 Get started today: search for Qwen3-VL in the Hugging Face Collection on Azure AI Foundry.
We’re thrilled to announce that the Qwen3-VL family of vision-language models is now available on Azure AI Foundry, thanks to our collaboration with Microsoft.
We bring open-source innovation to enterprise-grade AI infrastructure, making it easier than ever for enterprise to deploy and scale the latest and greatest from models from hugging Face securely within Azure.
🔍 Highlights:
- Deploy Qwen3-VL instantly via managed endpoints
- Built-in governance, telemetry, and lifecycle management
- True multimodal reasoning — vision, language, and code understanding
- State-of-the-art performance, outperforming closed-source models like Gemini 2.5 Pro and GPT-5
- Available in both *Instruct* and *Thinking* modes, across 24 model sizes
👉 Get started today: search for Qwen3-VL in the Hugging Face Collection on Azure AI Foundry.
- 1 reply
Post
861
What’s your biggest headache deploying Hugging Face models to the cloud—and how can we fix it for you?
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- 8 replies
Post
500
Post
3929
🤝 Collaborating with AMD to ensure Hugging Face Transformers runs smoothly on AMD GPUs!
We run daily CI on AMD MI325 to track the health of the most important model architectures and we’ve just made our internal dashboard public.
By making this easily accessible, we hope to spark community contributions and improve support for everyone!
We run daily CI on AMD MI325 to track the health of the most important model architectures and we’ve just made our internal dashboard public.
By making this easily accessible, we hope to spark community contributions and improve support for everyone!
-
- 2 replies
Post
18360
Okay this is insane... WebGPU-accelerated semantic video tracking, powered by DINOv3 and Transformers.js! 🤯
Demo (+ source code): webml-community/DINOv3-video-tracking
This will revolutionize AI-powered video editors... which can now run 100% locally in your browser, no server inference required (costs $0)! 😍
How does it work? 🤔
1️⃣ Generate and cache image features for each frame
2️⃣ Create a list of embeddings for selected patch(es)
3️⃣ Compute cosine similarity between each patch and the selected patch(es)
4️⃣ Highlight those whose score is above some threshold
... et voilà! 🥳
You can also make selections across frames to improve temporal consistency! This is super useful if the object changes its appearance slightly throughout the video.
Excited to see what the community builds with it!
Demo (+ source code): webml-community/DINOv3-video-tracking
This will revolutionize AI-powered video editors... which can now run 100% locally in your browser, no server inference required (costs $0)! 😍
How does it work? 🤔
1️⃣ Generate and cache image features for each frame
2️⃣ Create a list of embeddings for selected patch(es)
3️⃣ Compute cosine similarity between each patch and the selected patch(es)
4️⃣ Highlight those whose score is above some threshold
... et voilà! 🥳
You can also make selections across frames to improve temporal consistency! This is super useful if the object changes its appearance slightly throughout the video.
Excited to see what the community builds with it!
- 3 replies
Post
3226
We've improved the Deploy button on Hugging Face model pages for Microsoft Azure
1/ no more long waits before seeing model support status
2/ ready-to-use CLI and Python snippets
3/ redirection to Azure AI Foundry rather than Azure ML
✋ if you see any bugs or have feedback, open an issue on our repo:
https://github.com/huggingface/Microsoft-Azure
1/ no more long waits before seeing model support status
2/ ready-to-use CLI and Python snippets
3/ redirection to Azure AI Foundry rather than Azure ML
✋ if you see any bugs or have feedback, open an issue on our repo:
https://github.com/huggingface/Microsoft-Azure
Post
4787
The next generation of AI-powered websites is going to be WILD! 🤯
In-browser tool calling & MCP is finally here, allowing LLMs to interact with websites programmatically.
To show what's possible, I built a demo using Liquid AI's new LFM2 model, powered by 🤗 Transformers.js: LiquidAI/LFM2-WebGPU
As always, the demo is open source (which you can find under the "Files" tab), so I'm excited to see how the community builds upon this! 🚀
In-browser tool calling & MCP is finally here, allowing LLMs to interact with websites programmatically.
To show what's possible, I built a demo using Liquid AI's new LFM2 model, powered by 🤗 Transformers.js: LiquidAI/LFM2-WebGPU
As always, the demo is open source (which you can find under the "Files" tab), so I'm excited to see how the community builds upon this! 🚀
- 2 replies
Post
2197
Deploy GPT OSS models with Hugging Face on Azure AI!
We’re thrilled to enable OpenAI GPT OSS models on Azure AI Model Catalog for Azure users to try the model securely the day of its release.
In our official launch blogpost, there’s a section on how to deploy the model to your Azure AI Hub. Get started today!
https://huggingface.co/blog/welcome-openai-gpt-oss#azure
We’re thrilled to enable OpenAI GPT OSS models on Azure AI Model Catalog for Azure users to try the model securely the day of its release.
In our official launch blogpost, there’s a section on how to deploy the model to your Azure AI Hub. Get started today!
https://huggingface.co/blog/welcome-openai-gpt-oss#azure
Post
286
We now have the newest Open AI models available on the Dell Enterprise Hub!
We built the Dell Enterprise Hub to provide access to the latest and greatest model from the Hugging Face community to our on-prem customers. We’re happy to give secure access to this amazing contribution from Open AI on the day of its launch!
https://dell.huggingface.co/
We built the Dell Enterprise Hub to provide access to the latest and greatest model from the Hugging Face community to our on-prem customers. We’re happy to give secure access to this amazing contribution from Open AI on the day of its launch!
https://dell.huggingface.co/
Post
3601
Introducing Voxtral WebGPU: State-of-the-art audio transcription directly in your browser! 🤯
🗣️ Transcribe videos, meeting notes, songs and more
🔐 Runs on-device, meaning no data is sent to a server
🌎 Multilingual (8 languages)
🤗 Completely free (forever) & open source
That's right, we're running Mistral's new Voxtral-Mini-3B model 100% locally in-browser on WebGPU, powered by Transformers.js and ONNX Runtime Web! 🔥
Try it out yourself! 👇
webml-community/Voxtral-WebGPU
🗣️ Transcribe videos, meeting notes, songs and more
🔐 Runs on-device, meaning no data is sent to a server
🌎 Multilingual (8 languages)
🤗 Completely free (forever) & open source
That's right, we're running Mistral's new Voxtral-Mini-3B model 100% locally in-browser on WebGPU, powered by Transformers.js and ONNX Runtime Web! 🔥
Try it out yourself! 👇
webml-community/Voxtral-WebGPU
Post
373
🟪 Qwen/Qwen3‑235B‑A22B‑Instruct‑2507‑FP8 is now available in Microsoft Azure for one‑click deployment! 🚀
Check out their blogpost: https://qwenlm.github.io/blog/qwen3/
You can now find it in the Hugging Face Collection in Azure ML or Azure AI Foundry, along with 10k other Hugging Face models 🤗🤗
Qwen/Qwen3-235B-A22B-Instruct-2507-FP8
Bear with us for the non‑quantized version.
Check out their blogpost: https://qwenlm.github.io/blog/qwen3/
You can now find it in the Hugging Face Collection in Azure ML or Azure AI Foundry, along with 10k other Hugging Face models 🤗🤗
Qwen/Qwen3-235B-A22B-Instruct-2507-FP8
Bear with us for the non‑quantized version.
Post
1574
In our recent push to make more models available on Azure, we recently added SmolLM v3 in the catalog! 🚀
@juanjucm wrote a really detailed guide on how to deploy on Azure AI 🤗
https://huggingface.co/docs/microsoft-azure/azure-ai/examples/deploy-smollm3
If you want to see other models, please let us know
@juanjucm wrote a really detailed guide on how to deploy on Azure AI 🤗
https://huggingface.co/docs/microsoft-azure/azure-ai/examples/deploy-smollm3
If you want to see other models, please let us know
- 1 reply
Post
219
🎉 New in Azure Model Catalog: NVIDIA Parakeet TDT 0.6B V2
We're excited to welcome Parakeet TDT 0.6B V2—a state-of-the-art English speech-to-text model—to the Azure Foundry Model Catalog.
What is it?
A powerful ASR model built on the FastConformer-TDT architecture, offering:
🕒 Word-level timestamps
✍️ Automatic punctuation & capitalization
🔊 Strong performance across noisy and real-world audio
It runs with NeMo, NVIDIA’s optimized inference engine.
Want to give it a try? 🎧 You can test it with your own audio (up to 3 hours) on Hugging Face Spaces before deploying.If it fits your need, deploy easily from the Hugging Face Hub or Azure ML Studio with secure, scalable infrastructure!
📘 Learn more by following this guide written by @alvarobartt
https://huggingface.co/docs/microsoft-azure/azure-ai/examples/deploy-nvidia-parakeet-asr
We're excited to welcome Parakeet TDT 0.6B V2—a state-of-the-art English speech-to-text model—to the Azure Foundry Model Catalog.
What is it?
A powerful ASR model built on the FastConformer-TDT architecture, offering:
🕒 Word-level timestamps
✍️ Automatic punctuation & capitalization
🔊 Strong performance across noisy and real-world audio
It runs with NeMo, NVIDIA’s optimized inference engine.
Want to give it a try? 🎧 You can test it with your own audio (up to 3 hours) on Hugging Face Spaces before deploying.If it fits your need, deploy easily from the Hugging Face Hub or Azure ML Studio with secure, scalable infrastructure!
📘 Learn more by following this guide written by @alvarobartt
https://huggingface.co/docs/microsoft-azure/azure-ai/examples/deploy-nvidia-parakeet-asr
Post
1279
If you want to dive into how the HF team worked with @seungrokj at @AMD
to optimize kernels on MI300, you should give a read to our latest blog!
Such a great educational material for anyone curious about the world of optimizing low level ML.
https://huggingface.co/blog/mi300kernels
to optimize kernels on MI300, you should give a read to our latest blog!
Such a great educational material for anyone curious about the world of optimizing low level ML.
https://huggingface.co/blog/mi300kernels
Post
1646
In case you missed it, Hugging Face expanded its collaboration with Azure a few weeks ago with a curated catalog of 10,000 models, accessible from Azure AI Foundry and Azure ML!
@alvarobartt cooked during these last days to prepare the one and only documentation you need, if you wanted to deploy Hugging Face models on Azure. It comes with an FAQ, great guides and examples on how to deploy VLMs, LLMs, smolagents and more to come very soon.
We need your feedback: come help us and let us know what else you want to see, which model we should add to the collection, which model task we should prioritize adding, what else we should build a tutorial for. You’re just an issue away on our GitHub repo!
https://huggingface.co/docs/microsoft-azure/index
@alvarobartt cooked during these last days to prepare the one and only documentation you need, if you wanted to deploy Hugging Face models on Azure. It comes with an FAQ, great guides and examples on how to deploy VLMs, LLMs, smolagents and more to come very soon.
We need your feedback: come help us and let us know what else you want to see, which model we should add to the collection, which model task we should prioritize adding, what else we should build a tutorial for. You’re just an issue away on our GitHub repo!
https://huggingface.co/docs/microsoft-azure/index