Fresh vLLM containers can recompile thousands of Python files at startup. One build flag moves that work to image build time: 14–18% less startup time (~18 s) in my H100 benchmarks. Already in the CUDA nightly; official release images from v0.32.0. vllm.ai/blog/2026-09-22-pyth…

Fresh vLLM containers can recompile thousands of Python files at startup. One build flag moves that work to image build time: 14–18% less startup time (~18 s) in my H100 benchmarks. Already in the CUDA nightly; official release images from v0.32.0. vllm.ai/blog/2026-09-22-pyth…

Restarting a vLLM server and waiting for big quantized weights to load all over again? The new release goes after that wait. vLLM v0.31.0 landed late Sunday with 717 commits from 307 contributors. The piece I'd try first is the new vllm preload command, which starts a daemon that keeps post-quantized weights resident in GPU memory across engine restarts, with a /health endpoint and a readiness wait. There's also an experimental snapshot feature that uses CRIU to restore a fully initialized engine running on one GPU. The breaking-changes list is real: per-request mm_processor_kwargs and media_io_kwargs are now rejected unless you set --trust-request-mm-kwargs, tokenizer_mode="slow" is gone, and quantization="fp8" for online quantization is replaced by the fp8_per_tensor shorthand. Before you upgrade a production server, search your launch scripts and request code for every flag on that list. Then time a restart with and without preload so you know what you're actually saving. #vLLM github.com/vllm-project/vllm…