The default moved
vLLM switched every model to a new runner and deleted ten architectures on its way out
Version 0.29.0 makes Model Runner V2 the default for all models, turns FlashInfer all-reduce on by default, removes ten model architectures and deprecates the `api_server` entry point that sits in a great many Dockerfiles. NVIDIA’s TensorRT-LLM removed its own sampler the same morning.
| What moved | Before 0.29.0 | After |
|---|---|---|
| Model runner | MRV1 for most models | Model Runner V2 for all; MRV1 kept for a few ROCm models |
| All-reduce on TP CUDA groups | Off by default | FlashInfer, on by default; opt out with VLLM_ALLREDUCE_USE_FLASHINFER=0 |
| Arctic, Chameleon, Cheers, Fairseq2Llama, FireRedLID, GritLM, HCXVision, MPT and two aliases | Served | Removed |
| FlexOlmo, Olmo3, Hunyuan V1 and VL | Native vLLM path | Transformers modeling backend |
| python -m vllm.entrypoints.openai.api_server | Supported | Deprecated; use vllm serve |
| PyAV video decoder | Available | Removed; use OpenCV or Torchcodec |
vLLM 0.29.0 landed at 09:54 UTC on 9 September and made Model Runner V2 the default for every model. That finishes a rollout which began with pooling models alone.
This is an upgrade that changes what your server does rather than what it can do, and the release says so in a section headed Breaking changes.
Ten deprecated architectures are gone: Arctic, Chameleon, Cheers, Fairseq2Llama, FireRedLID, GritLM, HCXVision, MPT, and the `RWForCausalLM` and `StableLMEpochForCausalLM` aliases, with `PrithviGeoSpatialMAE` superseded by Terratorch. Three more, FlexOlmo, Olmo3 and Hunyuan V1 and VL, were not removed but moved onto the Transformers modeling backend, which is a different performance profile reached by the same model name.
Two further removals appear in no headline and will be felt more widely than the architecture list. The PyAV video decoder backend is gone, with OpenCV or Torchcodec named as replacements. And `python -m vllm.entrypoints.openai.api_server` is deprecated in favour of `vllm serve`, which is a line that has been copied into production Dockerfiles for years.
The default that will move numbers rather than break builds is FlashInfer all-reduce, now enabled for tensor-parallel CUDA groups. If it regresses on your topology the escape hatch is `VLLM_ALLREDUCE_USE_FLASHINFER=0`, which is worth knowing before you need it rather than after. Mamba prefix caching arrives with internal prefill checkpoints and a stated 9 to 25 per cent improvement in time to first token, and prefix-cache `NONE_HASH` is now deterministic by default, so distributed KV cache users no longer have to pin `PYTHONHASHSEED`.
The case for doing all this at once is that a runner transition half-finished is worse than either end of it, and the release carries 594 commits from 277 contributors, 91 of them new. The case against is that a single version changes the default execution path, the default collective and the supported model list together, so a regression after upgrading has three candidate causes before you start. The project chose the first and printed the list, which is the right order to do it in.
NVIDIA shipped its own removal 43 minutes later. TensorRT-LLM v1.3.0rc26 carries “Remove TRTLLMSampler (BREAKING)” along with the C++ decoder stack behind it, makes the Python KV cache transceiver the default, and adds a second breaking change nobody headlines in a redesign of VisualGen reference inputs and worker IPC. It is a release candidate and its known-issues list says so: LoRA adapters targeting the routed experts of Qwen3 mixture-of-experts models “are silently ignored and produce base-model output.”
The verdict on both: pin, read the breaking-changes section, then upgrade deliberately. A silently ignored LoRA adapter that returns fluent base-model text is the failure mode that will not page you.
What it changes Before you bump vLLM, grep your serving config for the ten removed architectures and your Dockerfiles for vllm.entrypoints.openai.api_server. If you are on TensorRT-LLM’s TRTLLM sampler, migrate before rc26 rather than after, and if you serve Qwen3 MoE with LoRA adapters, verify the adapter is actually being applied.
Primary source read