●Change8

Migrating to vLLM v0.21.0

Version v0.21.0 introduces 2 breaking changes. This guide details how to update your code.

Released: 5/14/2026

2
Breaking Changes
2
Migration Steps
24
Affected Symbols

⚠️ Check Your Code

If you use any of these symbols, you need to read this guide:

transformers v4RayExecutorV2Model Runner V2Gemma3/Gemma4DeepSeek V4Cohere reasoning/tool parsersLFM2/2.5 tool parserQwen3.5/Mamba hybrid model supportViT CUDA graphVendor HCXVisionConfiglegacy `rope_type` checkpointSchedulerOffloadingConnectorMooncakeStoreConnectorDCP/PCPIndexCacheFlashInfer samplerTurboQuantAllPool.forwardNVFP4MXFP4XGrammar 0.2.0TokenizerASR Engine

Breaking Changes

●Issue #1

vLLM now requires a C++20-compatible compiler for building due to PyTorch compatibility requirements. Users must update their compiler setup.

●Issue #2

Support for `transformers` v4 is formally deprecated. Users must migrate to `transformers` v5.

Migration Steps

  1. 1
    Update compiler to use C++20 standard.
  2. 2
    Migrate usage from `transformers` v4 to `transformers` v5.

Release Summary

This release introduces significant performance and stability improvements, notably integrating KV offloading with the Hybrid Memory Allocator and enabling speculative decoding with thinking budgets. It also formally deprecates support for older versions of the Transformers library.

Need More Details?

View the full release notes and all changes for vLLM v0.21.0.

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