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
- Cloud release velocity scores: AWS 95, Azure 90, Google Cloud 82 — DavidLinthicum · 2026-10-27
- Selling a 5090 to scale local inference: one business weighs 5 hardware paths — Anarchaotic · 2026-10-27
- AWS tutorial: fleet-wide edge CV model lifecycle with SageMaker SDK v3, ~$0.07 per run — AWS ML Blog · 2026-10-27
- Qwen3.8-27B hits 40 tok/s on Jetson Thor with INT4 + MTP, accuracy beats FP16 — No-Dragonfly6246 · 2026-10-27
- Celesto launches CI runners nearly 90% cheaper than GitHub Actions, with agent sandboxes — aniketmaurya · 2026-10-27
- Supabase founders on rebuilding the backend for a billion AI-built apps — Latent Space · 2026-10-27