AI infrastructure optimization is shifting focus from token generation to prefill processing, which handles input context before model output begins. Prefill and decode have different computational needs, leading companies like Lumai to advocate for specialized hardware architectures rather than using the same processors for both tasks. As context lengths grow and agentic workflows increase, prefill efficiency becomes critical to managing power budgets and inference economics in data centers.
A system successfully prefills a 284B parameter DeepSeek-V4-Flash model on NVIDIA DGX hardware and decodes it on Apple Silicon Mac Studio over standard 10GbE ethernet, achieving 1.5x to 3.7x speedup on prompts up to 241K tokens by computing the decoder's cache on the prefill machine rather than transferring incompatible KV cache formats.