DeepGEMM provides optimized CUDA kernels for general matrix multiplication using FP8 (8-bit floating point) precision, the fundamental compute operation that dominates both LLM training and inference. By reducing precision from the standard FP16 to FP8, these kernels roughly double throughput and halve memory bandwidth requirements while maintaining model quality through careful handling of the reduced dynamic range.
The kernels are specifically optimized for the matrix shapes and access patterns that occur in transformer model computation, including attention projections, feed-forward network layers, and the expert computations in MoE architectures. Rather than general-purpose FP8 GEMM implementations, DeepGEMM provides kernels tuned for the specific workloads that LLM serving requires, extracting performance that generic libraries leave on the table.
With over 6,300 GitHub stars, DeepGEMM completes DeepSeek's trilogy of open-source compute infrastructure alongside FlashMLA for attention and DeepEP for expert parallelism. Together these libraries provide the low-level compute primitives needed to train and serve large models with the efficiency that DeepSeek has demonstrated. The MIT license enables unrestricted use in both research and commercial inference deployments.
