#AMD MI355X

The engineering details on keeping long agent trajectories and hundreds of CPU sandboxes from stalling RL training are the real payload here, not the headline score. Teams building agentic RL pipelines can study how SGLang, Megatron-LM and Harbor were combined on AMD hardware as an alternative to NVIDIA-only stacks. It also shows Qwen3-30B-A3B reaching 27.5% on SWE-bench Verified with open training recipes.

Official accountLMSYS Org (SGLang)@lmsysorgSource

The MI355X's 288GB of HBM3E per GPU gives it an edge on very large models like the 1.6T-parameter DeepSeek V4 Pro, but serving-stack maturity on ROCm remains the bottleneck at higher loads. Teams evaluating accelerator purchases for agentic inference can now compare AMD and NVIDIA on realistic coding-agent workloads rather than synthetic throughput numbers. All results run on ROCm nightly builds of vLLM and SGLang, so scores should improve as the software stack matures.

Official accountArtificial Analysis@ArtificialAnlysSource