Accelerating Large-Scale LLM Inference on AMD Instinct MI350X/MI355X with Eagle3 and AMD Quark

Large language model (LLM) inference is increasingly constrained by autoregressive decoding. Even when prefill is highly optimized, the decode phase still generates tokens one step at a time, and each step typically requires running the full target model. For large mixture-of-experts and attention-heavy models such as Kimi-K2.5 and MiniMax-M2.5, this sequential pattern limits serving throughput and increases latency for real-time applications.