vLLM Integrates TileRT for Low-Latency Decoding
vllm_project · x · 2026-07-16
vLLM and TileRT demonstrated a concrete use case of a decoupled inference stack:
- By separating prefill and decode, the decode side can be freely swapped based on workload.
- TileRT connects to vLLM prefill via the vLLM V1 connector interface without modifying vLLM code, forking, or patching.
- It defaults to vLLM's native decode for throughput; for latency-sensitive scenarios, it can switch to TileRT decode, making it ideal for agents and real-time assistants.
- Both maintain OpenAI-compatible APIs and prefix caching, with the switch essentially being a routing-level choice.
- The text also mentions TileRT reported single-user decode speeds of roughly 618 tok/s on GLM-5.1-FP8, 8×B200 with MTP—nearly double its speed without MTP.
Related event: vLLM and TileRT Introduce Decoupled Inference Stack(3 posts)→
More from Infra
- Tabul AI launches Metal TreeSHAP to speed up Shapley values on Apple silicon — Scobleizer · 2026-07-22
- DeepSeek-V4-Flash tops out at 770 tok/s on one B300 in a vLLM batch test — Moreh · 2026-07-22
- NVIDIA starts shipping 102.4 Tbps Spectrum-6 switches for Vera Rubin AI factories — nvidia · 2026-07-22
- Apple publishes SOC 3 audit reports for Private Cloud Compute — throwfaraway4 · 2026-07-22
- Reddit GPU renters say existing platforms only give you two of three: code, recovery, fair billing — legendpizzasenpai · 2026-07-22
- The Sandboxing Manifesto: Secure Execution Environments for Agents — spirosoik · 2026-07-22