The Hugging Face community: popular models and datasets, leaderboard changes, a gauge of the open ecosystem.
Thinking Machines released Inkling on Hugging Face, a multimodal MoE model with around one trillion parameters, a one-million-token context and native image, text and audio inputs, alongside Inkling-Small with 276 billion total and 12 billion active parameters.
Hugging Face announced that vLLM’s transformers modelling backend now matches or exceeds the throughput of vLLM’s handwritten native implementations across several LLM architectures. It uses torch.fx for static graph analysis and ast to rewrite source code, dynamically applying inference-related layer fusion at runtime to match custom-code performance.
At Build 2026, Microsoft announced Foundry Managed Compute and a Hugging Face model collection, with weekly updates to open-weight models and one-click deployment to Foundry's managed GPU platform.
Hugging Face released LeRobot v0.6.0, introducing world model policies VLA-JEPA, FastWAM and LingBot-VA, new VLAs GR00T N1.7, MolmoAct2, EO-1, EVO1 and Multitask DiT, and a unified reward model API with Robometer and TOPReward for the robotics learning loop.
Hugging Face and SkyPilot released an integration allowing a Hugging Face Bucket or any model, dataset or Space repository to be mounted into SkyPilot tasks with an hf:// URL and an existing HF_TOKEN, running across more than 20 clouds, Kubernetes, Slurm and local environments.
Hugging Face and Cerebras jointly demonstrated a real-time speech-to-speech pipeline, accelerating Gemma 4 31B inference with Cerebras and combining Nvidia Parakeet speech recognition with Alibaba Qwen3TTS synthesis.
Google DeepMind released experimental open-source model DiffusionGemma, generating text blocks in parallel through text diffusion and achieving up to 4 times faster inference on dedicated GPUs.
Mistral AI released Voxtral speech-understanding models in 24B and 3B versions, both under Apache 2.0 and available via its API. With a 32k-token context, they handle up to 30 minutes of transcription or 40 minutes of audio understanding, including built-in question answering and summaries, automatic multilingual detection and voice-triggered function calls, inheriting Mistral Small 3.1's text capabilities.
Mistral AI released its first reasoning model, Magistral, comprising the 24B open-source Magistral Small and enterprise-focused Magistral Medium.