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

Mistral AI partners with NVIDIA to accelerate open frontier models

Long-Context Fine-Tuning with Limited VRAM

arXiv:2607. 15105v1 Announce Type: new Abstract: Parameter-efficient fine-tuning reduces model and optimizer memory, but dense attention still makes long training sequences expensive.

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

Less Data, Faster Convergence: Goal-Driven Data Optimization for Multimodal Instruction Tuning

arXiv:2603. 12478v2 Announce Type: replace-cross Abstract: Multimodal instruction tuning is often compute-inefficient because training budgets are spread across large mixed image-video pools whose utility is highly uneven.

FlashMLA-ETAP: Efficient Transpose Attention Pipeline for Accelerating MLA Inference on NVIDIA H20 GPUs

arXiv:2506. 01969v3 Announce Type: replace-cross Abstract: Efficient inference of Multi-Head Latent Attention (MLA) is challenged by deploying the DeepSeek-R1 671B model on a single Multi-GPU server.

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