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COMPANIES · 1 SOURCE · The Decoder

Mistral and Nvidia pair Mistral Large 4 with CUDA-X and Omniverse for physics AI

Mistral and Nvidia announced a partnership to combine Mistral Large 4 and Mistral's physics AI with Nvidia's CUDA‑X, Warp libraries and Omniverse to create an end‑to‑end workflow linking design, 3D simulation and AI training for industries like aerospace, automotive and semiconductors. They also plan to publish public benchmarks and open datasets; Mistral Large 4 is reported at about one trillion parameters, trained on 3,800 Grace Blackwell GPUs and due as an open‑weight model in late October.

8.0

COMPANIES · 1 SOURCE · Mistral AI

Mistral and NVIDIA partner to build AI-native engineering workflows and public physics benchmarks

Mistral and NVIDIA are collaborating to integrate Mistral Large 4 (Le Chonk) with NVIDIA accelerated computing (CUDA‑X, Warp) and Omniverse for GPU‑accelerated 3D simulation, aiming to speed simulations and enable closed‑loop AI-driven design. The partnership will produce public benchmarks and open datasets for physics models and agentic AI in CAE/EDA, expand NVIDIA‑powered infrastructure for Mistral, and builds on prior work including Mistral NeMo and the Nemotron Coalition; Mistral Large 4 (~1T total / 49B active params) was trained on ~3,800 Grace Blackwell GPUs and its weights are planned for release at the end of October.

8.0

CODING · 1 SOURCE · Hugging Face

NVIDIA demonstrates using Warp and MJWarp to run MuJoCo at GPU scale (up to 2,048 envs)

NVIDIA describes migrating a MuJoCo workflow (an SO-101 follower arm) to MJWarp, an implementation of MuJoCo’s physics pipeline in NVIDIA Warp, and demonstrates advancing as many as 2,048 parallel environments on NVIDIA GPUs. The article explains Warp’s GPU kernel model, device arrays, composable launches and graph capture, and notes Warp features such as differentiable kernels and an opt-in deterministic mode (Warp ≥ 1.15); it focuses on preparing and scaling simulation environments rather than training policies.

6.0