Mistral partners with NVIDIA to accelerate open frontier models

Mistral AI partners with NVIDIA to accelerate open frontier models

Mistral Advances AI for Engineering with NVIDIA

Mistral AI is partnering with NVIDIA to create a new generation of AI‑native engineering workflows that leverage NVIDIA’s accelerated computing capabilities. The collaboration aims to enhance engineering processes through advanced AI technologies.

Introducing Training Cluster as a Service - a new collaboration with NVIDIA

NVIDIA's GTC 2025 Announcement for Physical AI Developers: New Open Models and Datasets

Faster Training and Inference: Habana Gaudi®2 vs Nvidia A100 80GB

OpenAI and NVIDIA announce strategic partnership to deploy 10 gigawatts of NVIDIA systems

OpenAI and NVIDIA announce a strategic partnership to deploy 10 gigawatts of AI datacenters powered by NVIDIA systems, with the first phase launching in 2026.

How Much Progress Has There Been in NVIDIA Datacenter GPUs?

arXiv:2601. 20115v3 Announce Type: replace-cross Abstract: As the role of modern Graphics Processing Units (GPUs) becomes increasingly essential for several computing tasks, analyzing their past and current progress is paramount for determining future constraints on scientific research.

Accelerate ND-Parallel: A guide to Efficient Multi-GPU Training

Easily Train Models with H100 GPUs on NVIDIA DGX Cloud

GPU Parallelization Strategies for Forward and Backward Propagation in Shallow Neural Networks: A CUDA-Based Comparative Study

arXiv:2606. 30497v1 Announce Type: cross Abstract: We present a comparative study of CUDA optimization strategies applied to forward and backward propagation in a shallow neural network.

Speedrunning Tabular Foundation Model Pretraining

arXiv:2606. 03681v1 Announce Type: new Abstract: Pretraining cost is a major bottleneck for research on tabular foundation models, slowing the iteration cycle for new architectures, priors, and optimization ideas.

GGUF-Metadata Prediction of Single-Sequence llama.cpp Throughput Across Three Systems

The paper predicts single‑sequence llama.cpp throughput from GGUF metadata using roofline‑shaped predictors with quantization‑specific scale factors. Experiments on 318 measurements across 53 host‑file configurations on two Apple M4 Max systems and an NVIDIA RTX 5080 show that an active‑parameter decode model achieves significantly lower mean absolute percentage errors compared to models that use total parameters. The study also finds that low‑bit model ladders alter runtime ordering and that GGUF structure improves predictions, though fitted efficiencies vary across systems.