AMD on MI355X Inference: Multi-Token Prediction to Reduce Latency, Focus on Throughput in Interactive Ranges
AMD’s technical article outlines MI355X inference optimizations such as multi-token prediction, framing performance around throughput under interactive latency constraints.
AMD published a technical article describing inference performance optimization for its Instinct GPUs, highlighting MI355X and techniques such as multi-token prediction to reduce effective decode latency. The framing is increasingly common in production inference: what matters is not just peak throughput, but throughput under interactive latency constraints.
From an industry perspective, two signals stand out. First, real inference performance is now a software-and-hardware co-design problem, where compilers, kernels, scheduling, and runtime choices materially affect tokens per second. Second, evaluating accelerators should prioritize per-GPU throughput, cost, and energy efficiency across the target interactivity range, plus the maturity of the surrounding tooling.
As new accelerators enter cloud fleets, model-serving teams will run more workload-specific A/B tests. For MoE, long-context, and agentic pipelines, stability, observability, and operational complexity often weigh as much as benchmark curves in final platform selection.
Source: https://www.amd.com/en/developer/resources/technical-articles/2026/inference-performance-on-amd-gpus.html
Source: https://newsletter.semianalysis.com/p/inferencex-v2-nvidia-blackwell-vs