DeepGEMM: Efficiency and Precision in FP8 Matrix Multiplication on NVIDIA Hopper | Chatgpt and large language models in academia opportunities and challenges | Quick start guide to large language models pdf | Best large language models in the world | Turtles AI

 DeepGEMM is a specialized library for matrix multiplication in FP8 format, designed to maximize efficiency and accuracy on NVIDIA Hopper tensor cores. Written in CUDA and based on Just-In-Time compilation, it avoids complex dependencies, offering a streamlined, high-performance solution. It supports both standard GEMM and MoE, with advanced optimizations for handling numerical accumulation.

Key points:

• FP8 efficiency: Designed for matrix multiplication in FP8 with fine-grained scaling.
• CUDA optimization: Compact implementation in CUDA without the need for early compilation.
• Hopper compatibility: Leverages NVIDIA Hopper tensor cores with dual-level accumulation for higher accuracy.
• High Performance: Outperforms optimized libraries in specific inference scenarios, with open room for improvement.

DeepGEMM is a library designed to optimize matrix multiplication operations (GEMM) in FP8 format, meeting the computationally intensive needs typical of AI and machine learning. Written entirely in CUDA, it eliminates the need for a pre-compilation phase by leveraging a lightweight Just-In-Time (JIT) system to generate kernels directly at runtime. This approach allows for greater flexibility and ease of use, avoiding the complexities of integrating advanced templates or complex algebraic structures. The library is designed to be essential and highly accessible, with streamlined code that is reduced to a single function of about 300 lines, simplifying the analysis and customization of FP8 matrix multiplication techniques on Hopper tensor cores. An important aspect of DeepGEMM is the management of numerical accumulation. FP8 tensor cores, by their nature, suffer from accuracy problems due to the limited representation of values. To mitigate this critical issue, the library implements a two-level CUDA-core-based accumulation system, improving numerical stability without compromising performance. Although it draws inspiration from established frameworks such as CUTLASS and CuTe, DeepGEMM avoids depending directly on their templates, choosing a minimalist but effective approach. DeepGEMM’s scope extends to both standard GEMM operations and clustered computations typical of Mix-of-Experts (MoE) models, providing an appropriate level of scalability for advanced inference scenarios. Benchmarking tests, conducted on NVIDIA H800 SXM5 GPUs with the NVCC 12.8 compiler, show that the library achieves competitive performance, often outperforming optimized implementations based on CUTLASS 3.6. Evaluations focus on matrix configurations used in DeepSeek-V3/R1 models, including both prefilling and decoding stages, without enabling tensor parallelism. However, DeepGEMM shows room for improvement on some specific matrix shapes, making any community contributions for further optimizations welcome. The library structure is intentionally focused exclusively on GEMM kernels, delegating to developers the implementation of ancillary operations such as transposing or casting data to FP8. Support is currently limited to the NT format (with the LHS matrix untransposed and the RHS matrix transposed), while the scaling factor for the LHS must comply with a TMA alignment. DeepGEMM also offers some utility functions for PyTorch, although these may introduce slight performance penalties, since the library’s main focus remains on optimizing GEMM kernels.

This balance of efficiency, simplicity, and accessibility makes it a valuable resource for those wishing to learn more about optimization strategies for FP8 matrix multiplication in NVIDIA Hopper-based models.