The Inference Alpha: Maximizing Frontier Models on AMD

DigitalOcean's exploration into optimizing Large Language Models (LLMs) on AMD GPUs reveals significant performance enhancements and cost efficiencies through specialized inference engineering. By addressing systems-level challenges, such as model architecture, runtime execution, and memory systems, they demonstrate that achieving parity with more expensive hardware is possible. Advancements include deep kernel optimization and a customized inference framework, which led to substantial speed improvements, as exemplified by the Kimi 2.5 and DeepSeek V3.2 models. Additionally, the adoption of new formats like MXFP4, and techniques such as Multi-Head Latent Attention (MLA) and Mixture of Experts (MoE), has contributed to these gains by efficiently managing memory usage and compute tasks. These efforts not only enhance token throughput but also redefine the economic viability of deploying frontier models at scale, emphasizing a shift from generic software solutions towards tailored, high-performance AMD infrastructure.