DeepSeek R1 inference performance: MI300X vs. H200
DeepSeek-R1, with its innovative architecture combining Multi-head Latent Attention (MLA) and DeepSeekMoE, presents unique challenges for inference workloads. As a reasoning-focused model, it generates intermediate chain-of-thought outputs, placing significant demands on memory capacity and bandwidth.
In this benchmark, we evaluate the performance of three inference backends—SGLang, vLLM, and TensorRT-LLM—on two hardware configurations: 8x NVIDIA H200 and 8x AMD MI300X. Our goal is to compare throughput, latency, and overall efficiency to determine the optimal backend and hardware pairing for DeepSeek-R1's demanding requirements.
This benchmark was made possible through the generous support of our partners at Vultr and Lambda , who provided access to the necessary hardware.