Architecture-Aware LLM Inference Optimization on AMD Instinct GPUs: A Comprehensive Benchmark and Deployment Study

A production inference benchmark on AMD Instinct MI325X GPUs across large dense, MoE, MLA, and GQA model families using vLLM. GPU Hunter needs more than NVIDIA-only assumptions; AMD serving performance depends heavily on architecture-aware configuration.

This paper gives GPU Hunter a research-backed way to discuss AMD without assuming NVIDIA results transfer directly.

GPU Hunter needs more than NVIDIA-only assumptions; AMD serving performance depends heavily on architecture-aware configuration. The useful part for GPU Hunter readers is not the abstract result alone; it is the hardware implication: whether a model fits, whether a runtime can use the format, or whether throughput is limited by memory movement instead of arithmetic.

For AMD datacenter GPUs, model architecture, KV offload support, runtime kernels, and block size choices can dominate hardware specs. For shared inference, the important question is not only how fast one prompt runs. Batching, scheduling, cache placement, and request mix decide whether a GPU behaves like a reliable service.