NVIDIA H100 (Product): news timeline & CVEs

E3J: An Efficient and Open-Source Backend for Euclidean Equivariant Operations on GPU and TPU

Open-source e3j speeds Euclidean-equivariant GPU and TPU kernels, beating cuEquivariance on a MACE simulation.

e3j is an open-source JAX backend for Euclidean-equivariant geometric deep learning on GPU and TPU, using optimized CUDA and Pallas kernels. On a machine-learning interatomic potential workload it reports up to a 34% speed-up over cuEquivariance for water-box NPT simulation with MACE. Tensor-product operations exceed 80% of H100 peak memory bandwidth, and message-passing convolution forward throughput more than doubles earlier backends in many cases. A dedicated Pallas TPU kernel reaches over 80% of TPUv6e memory bandwidth, up to about ten times e3nn-jax, and the library is released under Apache 2.0.

arXiv cs.AI / cs.LG / cs.CLSep 28, 13:10 UTC · 3d agoAI tools & infra

SlideDP: Scaling Host-Resident LLM Fine-Tuning Across Multiple GPUs

SlideDP scales host-resident multi-GPU LLM fine-tuning, beating offload baselines and nearing FSDP2 on Qwen3-14B.

SlideDP is a synchronous data-parallel runtime that keeps one authoritative host state and pipelines parameter delivery, gradient aggregation, and CPU updates for full-parameter LLM fine-tuning beyond GPU memory. Matched-batch sweeps report geometric-mean throughput 1.46-2.64 times SlideFormer, MegaTrain, and ZeRO-Offload. On four H100s it approaches GPU-resident FSDP2 for Qwen3-14B and, with a larger batch, processes over 1 million tokens per step, 11.2% above FSDP2's measured peak. It also supports 256K-token sequences for that model and fine-tunes Qwen2.5-72B on four RTX 4090s.

Hugging Face daily papersSep 27, 20:00 UTC · 4d agoAI research

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E3J: An Efficient and Open-Source Backend for Euclidean Equivariant Operations on GPU and TPU

SlideDP: Scaling Host-Resident LLM Fine-Tuning Across Multiple GPUs