Architecture-Aware LLM Inference Optimization on AMD Instinct GPUs: A Comprehensive Benchmark and Deployment Study
New study reveals AMD GPUs can run massive AI models with 100% reliability under 1,000 concurrent users.
A new technical report by researcher Athos Georgiou provides the first comprehensive benchmark and deployment guide for running production-scale large language models on AMD's latest Instinct MI325X GPUs. The study tested four massive models—spanning 235 billion to 1 trillion parameters—across three architectural families (MoE+MLA, Dense+GQA, MoE+GQA) on an 8-GPU cluster with 2TB of HBM3e memory using the vLLM inference engine. The research reveals that architecture-aware optimization is essential: models using Multi-Head Latent Attention (MLA) require specific configurations like block size 1 and cannot use KV cache offloading, while those with Grouped Query Attention (GQA) benefit from both techniques.
Performance results show impressive throughput capabilities, with Qwen3-VL-235B reaching 47,873 tokens/second on vision workloads—6.5 times faster than Kimi-K2.5's 7,327 tokens/second. For text-only workloads, Llama-3.1-405B and DeepSeek V3.2 achieved comparable peak throughput (15,944 and 15,343 tokens/second respectively) despite an order-of-magnitude difference in active parameters. The study also examined AMD's AITER runtime acceleration, finding it provides modest 3-5% throughput benefits for general models but shows 2-16x higher measurement variability, confirming its optimization targets MoE/MLA kernels specifically.
All tested models demonstrated remarkable reliability, maintaining 100% HTTP-level success rates through 1,000 concurrent users while processing 18.9 million tokens across 17,406 requests without failures. The research identified common throughput saturation points consistent with memory-bandwidth bottlenecks (~500 concurrent users for short sequences, ~100-200 for longer sequences), providing crucial guidance for production deployment scaling. This 40-page report with 30 tables offers the most detailed public analysis to date of AMD GPU performance for cutting-edge AI inference workloads.
- AMD Instinct MI325X GPUs successfully ran models up to 1 trillion parameters with 100% reliability under 1,000 concurrent users
- Architecture-specific optimizations are critical: MLA models require block size 1 and no KV cache offloading, while GQA models benefit from both
- Qwen3-VL-235B achieved 47,873 tokens/second on vision workloads—6.5x faster than Kimi-K2.5 at 7,327 tokens/second
Why It Matters
Provides concrete evidence that AMD GPUs can compete with NVIDIA for production AI inference, offering enterprises more hardware options.