AMD MI300X GPU Performance Analysis

TL;DR

This paper evaluates the performance of AMD MI300X GPUs in large language model inference, comparing their compute, memory, and communication capabilities to NVIDIA GPUs, highlighting AMD's potential as a competitive alternative.

Contribution

It provides a comprehensive performance analysis of AMD MI300X GPUs for LLM inference, filling a gap in comparative hardware evaluations.

Findings

AMD MI300X GPUs show competitive compute throughput.

Memory bandwidth performance is high and suitable for LLMs.

Interconnect communication supports scalable LLM deployment.

Abstract

The rapid growth of large language models (LLMs) has driven the need for high-performance, scalable GPU hardware capable of efficiently serving models with hundreds of billions of parameters. While NVIDIA GPUs have traditionally dominated LLM deployments due to their mature CUDA software stack and state-of the-art accelerators, AMD's latest MI300X GPUs offer a compelling alternative, featuring high HBM capacity, matrix cores, and their proprietary interconnect. In this paper, we present a comprehensive evaluation of the AMD MI300X GPUs across key performance domains critical to LLM inference including compute throughput, memory bandwidth, and interconnect communication.