vLLM and TileRT now share a serving stack, splitting prefill and decode between two engines with opposite goals

vLLM x TileRT：两个目标相反的推理引擎，为什么开始共用一套服务

vLLM handles high-throughput batching; TileRT optimizes batch=1 decode for lower per-token latency. On July 14, vLLM's blog announced a disaggregated integration: vLLM runs prefill and first-token generation, then routes latency-sensitive requests to TileRT via MultiConnector. The connector opens the path, but the platform must decide which requests qualify. TileRT 0.1.5 runs only one in-flight request per 8×B200 node—fast but capacity-constrained. No independent end-to-end benchmarks cover the KV-cache transfer overhead yet. The piece argues inference serving is shifting from single-engine optimization to orchestrating specialized execution paths, with SGLang Omni and TensorRT-LLM pursuing similar specialization in their own domains.

Why it matters: vLLM and TileRT's disaggregated integration pushes inference serving from single-engine competition to specialized path orchestration, with concrete mechanism and code. Deduction: this is an official blog announcing the integration, no large-scale production validation data ye...