@alvarobartt published a step-by-step guide to deploy zai-org/GLM-5.2 on AMD GPUs, using newly released features in Microsoft Foundry. The FP8 model fits on a single node of MI300X GPUs, which cuts the bill in half vs. H100.
Open agents on AWS SageMaker AI with open models from the Hugging Face Hub!
> Deploy an open model from the Hugging Face Hub on SageMaker AI > Connect the deployed model to Strands Agents > Add built-in and custom tools for tool calling > Expose external capabilities through MCP integration > Bonus: talk to your agent and visualize traces with Gradio
Latest hf-mem release added a breakdown of Mixture-of-Experts (MoE) memory usage!
TL; DR MoEs can be misleading to reason about from active parameters alone, since each token only activates a subset of experts, while the serving setup still needs to account for the full resident memory footprint.
🧠 hf-mem now splits MoE memory into base model weights, routed experts, and KV cache 🏗️ Dense models usually load and use most weights every forward pass, while MoEs load many experts but only route each token to a few of them ⚡ Active params isn't the same as memory footprint, especially for sparse architectures 📦 Runtime memory is about what is used per request/token, while loading memory also includes the expert weights that need to be resident 📚 KV cache can still dominate depending on context length, batch size, and concurrency 🔀 Expert Parallelism (EP) helps shard experts across accelerators when expert weights dominate 🚀 Data Parallelism (DP) + EP is often a good fit for throughput-oriented MoE serving
Learn how to deploy Microsoft Research VibeVoice ASR on Microsoft Azure Foundry with Hugging Face to generate rich audio transcriptions with Who, When, and What! 💥
> 🕒 60-minute single-pass processing, no chunking or stitching > 👤 Customized hotwords to guide recognition on domain-specific content > 📝 Rich transcription: joint ASR + diarization + timestamping in one pass > 🌍 50+ languages with automatic detection and code-switching support > 🤗 Deployed on Microsoft Foundry via an OpenAI-compatible Chat Completions API
- 🏆 30B-A3B MoE, the strongest model in the 30B class. It excels at coding tasks, agentic workflows and reasoning. - 🤏🏻 Lighter version of his 358B big brother, balancing performance and efficiency.
💥 hf-mem v0.4.1 now also estimates KV cache memory requirements for any context length and batch size with the --experimental flag!
uvx hf-mem --model-id ... --experimental will automatically pull the required information from the Hugging Face Hub to include the KV cache estimation, when applicable.
💡 Alternatively, you can also set the --max-model-len, --batch-size and --kv-cache-dtype arguments (à la vLLM) manually if preferred.
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!
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 🤯
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 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!
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
🎉 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
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!
