Recent analysis challenges the growing industry trend of separating prefill and decode onto distinct GPU pools. Researchers argue this disaggregation only delivers clear throughput gains at hyperscale deployments with extremely large GPU fleets. Studies of real-world traffic show that for typical mixed workloads, network KV cache transfers, queueing delays, and integer GPU allocation constraints erode theoretical benefits. At modest GPU counts, disaggregated setups often match or underperform colocated configurations. Experts recommend chunked prefill on shared GPU pools as a more practical default, stabilizing time-per-output-token and reducing interference without extra networking or operational complexity, while reserving full disaggregation for thousand-GPU, high-bandwidth environments.
This update represents a notable development in the Ai sector. Organizations and founders tracking this space should evaluate potential strategic and technical implications on their operations.