DeepSeek's latest DeepGEMM update adds Mega MoE, a fused GPU kernel that collapses the entire Mixture‑of‑Experts pipeline and overlaps computation with NVLink communication, while also unveiling an FP4 indexer and FP8×FP4 precision experiments, signaling a push toward highly efficient large‑scale AI training.
This article presents a comprehensive benchmark of DeepGEMM, Cutlass, and Triton on NVIDIA H20 and H800 GPUs, analyzing TFLOPS, bandwidth, latency, and speedup across various matrix sizes, and concludes which library is optimal for different workload scenarios.
The article analyzes DeepSeek’s three open‑source projects—FlashMLA, DeepEP, and DeepGEMM—showing how they optimize for the China‑only NVIDIA H800 GPU, contrast this with the abundant hardware resources of Western AI firms, and highlight the growing demand for talent that masters both AI models and GPU hardware.
DeepGEMM is an open‑source FP8‑precision GEMM library that delivers up to 1350 TFLOPS on NVIDIA Hopper GPUs, offering JIT‑compiled, lightweight code (~300 lines) for dense and MoE matrix multiplication, with easy deployment, configurable environment variables, and performance advantages over CUTLASS for large AI models.
