LLM Inference Phase Separation Technology: The Path to Heterogeneous Computing Optimization for Prefill and Decode Phases

In the production deployment of LLMs, inference efficiency is a key bottleneck restricting application implementation. Phase separation technology optimizes throughput, latency, and cost by separating the prefill and decode phases to run on different hardware resources. This article combines cutting-edge research such as Splitwise and DistServe to analyze the background, methods, benefits, and ecosystem development of this technology.

LLM inference consists of two phases: prefill (processing the complete prompt, compute-intensive) and decode (generating tokens one by one, memory bandwidth-limited). Traditional co-location on the same GPU leads to unbalanced resource utilization: prefill seizes compute units, decode suffers from bandwidth competition, latency increases by 2-5 times, and batch processing strategies are difficult to optimize.

Splitwise deploys prefill on high-compute GPUs (e.g., H100) and decode on cheaper older GPUs (e.g., A100). By optimizing layer-wise transmission of KV Cache, the overhead is controlled within 0.1% of end-to-end latency, balancing performance and cost.

DistServe dynamically selects parallel strategies for the decode phase: tensor parallelism for latency-sensitive scenarios, and pipeline parallelism for throughput-priority scenarios. Through collaborative optimization of parallelism, combined with resource allocation, batch size, and scheduling strategies, it achieves a globally optimal configuration.

Phase separation brings multiple benefits: throughput increases by 2-7 times; cost reduces by 30-50%; decode latency fluctuations decrease; resource utilization is balanced, reducing idling and contention.

Phase separation is moving toward practice; mainstream inference frameworks such as vLLM, Sarathi, Orca, and SGLang have explored or supported this technology. vLLM integrates continuous batching and phase separation optimization, while SGLang provides an engineering foundation through flexible scheduling.

Large-scale deployment of phase separation faces challenges: increased system complexity, need for KV Cache transmission tuning, and synergy with technologies like speculative decoding. However, this technology is an important direction for LLM inference optimization and is expected to drive more efficient large model service infrastructure.