Disaggregation Is a Thousand-GPU Problem

DISAGGREGATION: A THOUSAND-GPU SOLUTION FOR INFERENCE FRAMEWORKS

Disaggregation has emerged as a pivotal strategy in enhancing the performance of inference frameworks, particularly with the recent advancements that allow for the separation of prefill and decode processes. This year, major frameworks have integrated disaggregation features, with NVIDIA incorporating it into Dynamo and SGLang making it the default for large-scale deployments. The consensus among industry experts is increasingly leaning towards the notion that splitting prefill and decode onto distinct GPU pools can significantly improve throughput. However, the effectiveness of disaggregation is primarily contingent on the scale of GPU deployment, particularly when dealing with a thousand-GPU problem.

HOW DISAGGREGATION IMPACTS PREFILL AND DECODE PERFORMANCE

The implementation of disaggregation has shown promising results in enhancing prefill and decode performance. According to Doubleword's analysis, a balanced disaggregated deployment can achieve throughput levels comparable to colocated setups. However, the benefits of this approach become more pronounced in larger GPU counts. At smaller scales, the rounding losses associated with fractional GPU allocation can undermine the potential gains from specialization. This indicates that while throughput improvements are achievable through disaggregation, the practical advantages at lower GPU counts may not be as significant as anticipated, focusing instead on independent service level objective (SLO) tuning rather than throughput.

IS DISAGGREGATION WORTH IT FOR SMALL GPU COUNTS?

When evaluating the worthiness of disaggregation for small GPU counts, the analysis suggests that the benefits may not justify the complexity introduced by such a deployment. A June 2025 study, which assessed hundreds of thousands of design points, indicated that disaggregation proves most effective under prefill-heavy traffic patterns and with larger models. For the mixed-traffic workloads that many teams encounter, issues such as queueing and inter-node KV cache transfer tend to dominate end-to-end latency. Thus, while disaggregation can enhance performance in certain contexts, for smaller GPU deployments, the challenges and potential drawbacks may outweigh the benefits.

THE ROLE OF DISAGGREGATION IN OPTIMIZING INFERENCE WORKLOADS

Disaggregation plays a critical role in optimizing inference workloads by allowing teams to tailor their GPU resources more effectively. As organizations increasingly adopt disaggregated architectures, they can better manage their workloads, particularly in scenarios where prefill-heavy traffic is prevalent. However, it is essential to recognize that disaggregation does not eliminate bottlenecks; rather, it shifts them. Teams that have implemented disaggregation often find that while they may improve certain aspects of performance, they still need to address new latency challenges that arise from the separation of processes. This necessitates a careful balance between the advantages of disaggregation and the inherent complexities it introduces.

IS CHUNKED PREFILL THE BETTER DEFAULT FOR DISAGGREGATED DEPLOYMENTS?

In light of the challenges associated with disaggregation, the question arises: is chunked prefill a more effective default for disaggregated deployments? The experience of many teams suggests that enabling chunked prefill on the same GPU pool can stabilize performance, particularly under bursty traffic conditions. For example, in a recent case involving an inference workload serving a mid-size classification model, the initial instinct to separate prefill from decode led to instability. However, by opting for chunked prefill instead, the performance stabilized significantly. This indicates that for certain workloads, particularly those with less predictable traffic patterns, chunked prefill may indeed provide a more reliable and efficient solution than disaggregation.