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chore(deps): bump vllm from 0.26.0 to 0.28.0 in /backend/python/vllm - #11752

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mudler merged 1 commit into
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dependabot/pip/backend/python/vllm/vllm-0.28.0
Aug 27, 2026
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chore(deps): bump vllm from 0.26.0 to 0.28.0 in /backend/python/vllm#11752
mudler merged 1 commit into
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dependabot/pip/backend/python/vllm/vllm-0.28.0

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Bumps vllm from 0.26.0 to 0.28.0.

Release notes

Sourced from vllm's releases.

v0.28.0

Highlights

This release features 584 commits from 270 contributors (76 new)!

  • Kimi-K3 performance push: a major optimization effort for Kimi-K3 across the stack — Decode Context Parallel (DCP) support (#50484), fused FlashKDA decode and prefill kernels (#50654, #51311, #52458), SiTU activation support for MegaMoE (#50510), GEMM-RS for sequence parallelism (#52079), combined all-gathers with 1.5~3x kernel-level speedup (#51070), an adaptive speculative token budget delivering ~60% better DSpark TTFT (#51725), and optional shared-expert sharding saving ~17 GiB of memory per GPU (#50912). Kimi-K3 also now runs on ROCm with the V2 model runner (#51653).
  • DeepSeek V4: sparse MLA now works end-to-end for plain decode, MTP, and DSpark speculative decoding (#51538), joined by AMD Quark NVFP4 support (#47972), reasoning-effort prompts and mappings (#50580), sparse top-k metadata kernel optimizations (#52084, #51967), narrowed eager CUDA graph regions (#51430, #52401), and ROCm enablement on gfx11 and gfx950 (#47017, #52212).
  • Speculative decoding advances: DFlash2 with local convolution and a candidate selector (#52816), DSpark confidence-scheduled verification (#47808), and async scheduling auto-enabled for draft models (#48341).
  • Model Runner V2 maturation: E/P/D disaggregation (#38390), weight offloading (#51413), multi-layer MTP KV cache support (#50062), encoder CUDA graphs (#49852), decoder token-wise pooling (#50931) plus Transformers pooling models (#52425), attention-free models (#52374), and thinking_token_budget support (#46727).
  • Tiered KV cache offloading: disk offloading support (#49644), out-of-tree secondary tier managers via module_path (#51007), partial secondary-tier load results (#50321), tiering metrics (#48798), and a canonical CPU layout for parallelism-agnostic offload (#48414).
  • Rust frontend & gRPC: a standalone renderer (#50289), multimodal image inference over gRPC (#50368), explicit data-parallel rank routing (#51178), and RL lifecycle control (#51316), with protobuf schemas now published to Buf (#51276).
  • New defaults: max_num_batched_tokens raised from 8192 to 16384 (#51726), prefix caching enabled by default for Mamba models (#50991), and the Blackwell CUDA graph capture default raised to 1024 (#49390).
  • Breaking changes: bitsandbytes support migrated to an out-of-tree plugin (#43529); Transformers bumped to 5.15.0 (#51668); the deprecated calculate_kv_scales runtime KV scale calculation was removed (#49389); override_attention_dtype was removed (#48684).

Release Artifacts

Python Wheels

Platform Install
PyPI (CUDA 13.0) pip install vllm
PyPI (CUDA 13.0, uv) uv pip install vllm --torch-backend=auto
ROCm pip install vllm --extra-index-url https://wheels.vllm.ai/rocm/0.28.0/rocm722

Docker Images

Platform Docker Image
CUDA 13.0 (Default) docker pull vllm/vllm-openai:v0.28.0 (v0.28.0-cu130 also works)
CUDA 12.9 docker pull vllm/vllm-openai:v0.28.0-cu129
CUDA 13.0 + Ubuntu 24.04 docker pull vllm/vllm-openai:v0.28.0-ubuntu2404
CUDA 12.9 + Ubuntu 24.04 docker pull vllm/vllm-openai:v0.28.0-cu129-ubuntu2404
ROCm docker pull vllm/vllm-openai-rocm:v0.28.0
CPU docker pull vllm/vllm-openai-cpu:v0.28.0
XPU docker pull vllm/vllm-openai-xpu:v0.28.0

Other Artifacts

Pre-built release artifacts are available in the Assets section at the bottom of this page, including:

  • Source distribution tarball
  • CUDA 12.9 Python wheels for x86_64 and arm64
  • CUDA 13.0 Python wheels for x86_64 and arm64
  • CPU Python wheels for x86_64, arm64, and macOS

Model Support

  • New models: Muse Glimmer (#51655), Ling 3.0 Flash with BF16, MTP, and parser support (#51045) plus an FP8 variant (#51265) and hybrid MXFP4 routed experts (#52114), Dots3 NOTE native multimodal support (#51255), and Interns2mobius (#51149).
  • Qwen: Qwen3.8 enabled on AMD ROCm (#50068), fused CUDA post-conv MTP decode kernel for Qwen3.5 GDN (#51674), GDN gates aligned with speculative tokens (#51812), and Qwen3.5 fixes for text-only checkpoints (#50734, #50355).
  • Transformers modeling backend: MLA support (#48250), hardware-agnostic model definition (#49458), fully generalized input embedding handling (#51247), logit softcapping (#52173), and a hardened multimodal path (#51408, #51657).
  • LoRA: vision tower LoRA for Gemma4 (#42662), tower/connector LoRA for Keye (#51780) and Ultravox (#48215).

... (truncated)

Commits
  • 2cf0a69 [CI/Build] Pin Cython below 3.3 for arm64 tilelang sdist (#53358)
  • 9991487 Revert "[Kernel] Gemma-4 FA4 FP8 Kernel" (#52987)
  • 1af5a38 [CI][Release] Extend DSv4 engine readiness timeout (#53252)
  • 74a6576 [Spec Decode] DFlash2: local convolution + candidate selector (#52816)
  • cf902bd [CI/Build] Fix accident pre-commit breakage due to concurrent merge (#52578)
  • cd6ae1e [Bugfix][Security] Guard _load_ov2_processor with resolve_trust_remote_code (...
  • b7d8e0f [kv_offload] fix(metrics): rename kv_offload_tiering_block_{queries,hits} → c...
  • c0eac6f [ROCm]: Bump triton 3.7 commit (#52819)
  • 855e09e [Kernel] SM120: stop routing misaligned-M blockwise FP8 GEMMs to the small-M ...
  • 1b6ed9b [EPD] Allow KV consumers to omit MM embeddings (#52697)
  • Additional commits viewable in compare view

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Bumps [vllm](https://github.com/vllm-project/vllm) from 0.26.0 to 0.28.0.
- [Release notes](https://github.com/vllm-project/vllm/releases)
- [Changelog](https://github.com/vllm-project/vllm/blob/main/RELEASE.md)
- [Commits](vllm-project/vllm@v0.26.0...v0.28.0)

---
updated-dependencies:
- dependency-name: vllm
  dependency-version: 0.28.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

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The vLLM 0.28.0 pin update is scoped to the L4T CUDA 13 requirements, and the full vLLM backend build matrix is green across CPU, CUDA 12/13, L4T arm64, ROCm, Intel, and Darwin. Good to merge from my side. @mudler

@mudler
mudler merged commit 92bcbae into master Aug 27, 2026
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@mudler
mudler deleted the dependabot/pip/backend/python/vllm/vllm-0.28.0 branch August 27, 2026 20:32
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