Accelerating LLM Inference on AMD GPUs with Low-Latency GEMMs
AMD introduces optimized low-latency GEMM kernels for LLM inference on AMD GPUs, reducing prefill and decode latency by up to 3.5x compared to standard implementations. The optimizations target memory-bound operations in transformer-based models, leveraging hardware-specific features to improve throughput and responsiveness in AI workloads.
Background
- GEMM stands for General Matrix Multiply — the fundamental mathematical operation behind neural network computations, especially in large language models (LLMs) like GPT, Llama, or Claude.
- AMD is a major chipmaker that competes with NVIDIA. While NVIDIA's GPUs (especially the H100) dominate AI hardware, AMD is trying to catch up with its ROCm software stack and GPUs like the MI300X.
- "Low-latency GEMMs" refers to specialized, optimized matrix multiplication routines that reduce the time per computation step. In LLM inference (i.e., running the model to generate text, not training it), every millisecond matters because the model must process each token (word piece) sequentially — so faster math means faster responses.
- This blog post from AMD's ROCm team announces new library-level optimizations to make LLM inference faster on AMD GPUs. It is part of AMD's ongoing effort to offer a credible alternative to NVIDIA in the AI ecosystem, which currently relies heavily on NVIDIA's CUDA platform and its cuBLAS/cuBLASLt libraries for fast matrix math.