v0.13.0

# vLLM v0.13.0 Release Notes Highlights

## Highlights

This release features **442 commits from 207 contributors (61 new contributors)!**

**Breaking Changes**: This release includes deprecation removals, PassConfig flag renames, and attention configuration changes from environment variables to CLI arguments. Please review the breaking changes section carefully before upgrading.

### Model Support
* **New models**: BAGEL (AR only) (#28439), AudioFlamingo3 (#30539), JAIS 2 (#30188), latent MoE architecture support (#30203).
* **Tool parsers**: DeepSeek-V3.2 (#29848), Gigachat 3 (#29905), Holo2 reasoning (#30048).
* **Model enhancements**: Qwen3-VL embeddings support (#30037), Qwen3-VL EVS (Efficient Video Sampling) (#29752), DeepSeek V3.2 proper `drop_thinking` logic (#30490), DeepSeek V3.2 top-k fix (#27568).
* **Task expansion**: Automatic TokenClassification model conversion (#30666), Ultravox v0.7 transformer projector (#30089).
* **Quantization**: BitsAndBytes for Qwen3-Omni-MoE (#29896).
* **Speculative decoding**: Eagle/Eagle3 Transformers backend (#30340), Mamba `selective_state_update` spec decode (#29488).

### Engine Core
* **Compilation**: Conditional compilation via `compile_ranges` for selective kernel compilation (#24252).
* **Prefix caching**: xxHash high-performance hash option (#29163).
* **Attention**: PrefixLM support for FlexAttention (#27938) and TritonAttention (#30386), CUDA graphs for 3D Triton attention (#28306), `TRITON_MLA` without prefix-caching (#29125).
* **Batch invariance**: FA2 and LoRA batch-invariant support (#30018).
* **Pooling**: Chunked prefill for ALL pooling tasks (#27145), multi-vector retrieval API (#26686).
* **Model Runner V2**: Min-p sampling (#30171), NaN detection in logits (#30187).
* **Speculative decoding**: Medusa GPU-CPU sync avoidance (#29723), async spec-decode improvements (#29624).
* **Whisper**: Major performance improvements - [V1 is now faster than V0](https://github.com/vllm-project/vllm/issues/24946#issuecomment-3680725754) (~3x speedup vs v0.12.0). Encoder batching (#29421), `FULL_DECODE_ONLY` CUDA graph (#30072), CPU backend support (#30062).
* **Performance**: Fused blockwise quant RMS norm (#27883), MoE LoRA loading reduction (#30243), encoder cache optimization (#30475), CPU KV offloading streams (#29013).

### Hardware & Performance
* **NVIDIA Blackwell Ultra**: SM103 (GB300) support with CUDA 13 (#30484).
* **DeepSeek optimizations** (benchmarked on DeepSeek-V3.1):
  - DeepEP High-Throughput CUDA graph enabled by default: **5.3% throughput, 4.4% TTFT improvement** (#29558)
  - DeepGEMM fused layout kernel: **4.3% throughput, 10.7% TTFT improvement** (#29546)
  - DeepGEMM experts initialization: **3.9% TTFT improvement** (#30494)
  - `group_topk` kernel: **1.9% throughput, 2.1% TPOT improvement** (#30159)
  - Sparse prefill kernel for FP8 KV-cache in DeepSeek-V3.2 (#27532)
  - MLA FP8 optimization with ReduceScatterSum (#29795), direct k_nope/k_pe copy (#29710)
* **CPU**: Whisper support (#30062), Arm Optimized Routines vectorized exp (#30068), x86 CPU wheel pipeline (#28848).
* **AMD ROCm**: Aiter quantization kernels (#25552), torch.compile layernorm/silu + FP8 quant (#25693), Triton ScaledMM fallback (#26668), MXFP4 w4a4 inference (#29775).
* **Intel XPU**: wNa16 compressed tensors (#29484).
* **Build**: CUDA 13 aarch64 wheels (#30341), Docker kernel build stage (#29452), Ascend NPU Docker (#30015).

### Large Scale Serving & Disaggregated Prefill/Decode
* **KV connectors**: Mooncake Transfer Engine (#24718), cache reset via `/reset_prefix_cache` (#27170), KV events (#28309), failure recovery config (#26813).
* **NIXL**: Compatibility checking in handshake (#29503), large batch proxy support (#28782).
* **EPLB**: NVFP4 support (#29804), algorithm abstraction (#26471).
* **Multi-node**: External launcher mode (#29833).
* **Hybrid allocator**: Optional KV connector integration (#29805).
* **Performance**: silu_mul_per_token_group_quant_fp8 kernel for DP/EP (#29470).

### Quantization
* **New**: W4A8 grouped GEMM on Hopper (#29691), online FP8 with streaming post-processing (#29196), FP8 weight reloading for RLHF (#28480).
* **MoE + LoRA**: AWQ Marlin (#30442) and GPTQ Marlin (#30254) support.
* **GGUF**: MoE + GGUF restored for Qwen3 MoE (#30116), Qwen2 MoE (#30307), HF defaults override (#30118).
* **Compatibility**: Transformers v5 RoPE support (#30046).

### API & Frontend
* **Responses API**: MCP type infrastructure (#30054), Browser/Container MCP tools (#29989), full MCP Python loop (#29798), extra body parameters (#30532).
* **Configuration**: `AttentionConfig` replaces `VLLM_ATTENTION_BACKEND` env var (#26315).
* **Chat templates**: DeepSeek-V3.2 (#29837), DeepSeek-V3.2 developer tools (#30040).
* **Anthropic API**: Streaming fixes (#29971, #30266).
* **Embeddings**: Binary format with `encoding_format=bytes_only` (#30249), multiple image/audio per request (#29988), tokenization_kwargs override (#29794).
* **Metrics**: Prefill KV compute metric excluding cached tokens (#30189).
* **Profiling**: Layer-wise NVTX (#29990), profiling CLI config (#29912).
* **UX**: Better OOM errors (#28051), ModelConfig validation (#30213), distributed executor errors (#30140).

### Security
* Additional protection for CVE-2025-62164 (#30649).

### Dependencies
* NVSHMEM 3.3.24 + CUDA 13 fix (#30149).
* TPU tpu-inference 0.12.0 (#30221).

### Breaking Changes & Deprecations
1. **PassConfig flags renamed** per RFC #27995 (#29646)
2. **Attention env vars → CLI args**: `VLLM_ATTENTION_BACKEND` replaced with `--attention-backend` (#26315)
3. **Removed `-O.xx` flag** (#29991)
4. **Removed deprecated plugin/compilation fields** (#30396)
5. **Removed deprecated task, seed, MM settings** (#30397)
6. **Removed `embed_input_ids`/`embed_multimodal` fallbacks** (#30458)
7. **Removed tokenizer setter** (#30400)
8. **Deprecations**: `merge_by_field_config` (#30035, #30170), `--convert reward` → `--convert embed` (#30463)

## New Contributors 🎉

* @ajpqs made their first contribution in https://github.com/vllm-project/vllm/pull/29905
* @amitz-nv made their first contribution in https://github.com/vllm-project/vllm/pull/29978
* @amrmahdi made their first contribution in https://github.com/vllm-project/vllm/pull/29452
* @andrewbriand made their first contribution in https://github.com/vllm-project/vllm/pull/29804
* @anker-c2 made their first contribution in https://github.com/vllm-project/vllm/pull/30344
* @AuruTus made their first contribution in https://github.com/vllm-project/vllm/pull/30182
* @avigny made their first contribution in https://github.com/vllm-project/vllm/pull/19425
* @Bhanu068 made their first contribution in https://github.com/vllm-project/vllm/pull/30254
* @Copilot made their first contribution in https://github.com/vllm-project/vllm/pull/29025
* @dbotwinick made their first contribution in https://github.com/vllm-project/vllm/pull/30583
* @dependabot[bot] made their first contribution in https://github.com/vllm-project/vllm/pull/30234
* @desertfire made their first contribution in https://github.com/vllm-project/vllm/pull/29919
* @dmitry-tokarev-nv made their first contribution in https://github.com/vllm-project/vllm/pull/30149
* @drslark made their first contribution in https://github.com/vllm-project/vllm/pull/30632
* @dtcccc made their first contribution in https://github.com/vllm-project/vllm/pull/24718
* @elizabetht made their first contribution in https://github.com/vllm-project/vllm/pull/28671
* @Elm8116 made their first contribution in https://github.com/vllm-project/vllm/pull/30068
* @gausah01 made their first contribution in https://github.com/vllm-project/vllm/pull/29604
* @gh-wf made their first contribution in https://github.com/vllm-project/vllm/pull/30285
* @hdlj-h made their first contribution in https://github.com/vllm-project/vllm/pull/30056
* @HF-001 made their first contribution in https://github.com/vllm-project/vllm/pull/30051
* @hzxuzhonghu made their first contribution in https://github.com/vllm-project/vllm/pull/29931
* @JaviS-Rei made their first contribution in https://github.com/vllm-project/vllm/pull/29882
* @johannesflommersfeld made their first contribution in https://github.com/vllm-project/vllm/pull/30390
* @KevinMusgrave made their first contribution in https://github.com/vllm-project/vllm/pull/30529
* @kitaekatt made their first contribution in https://github.com/vllm-project/vllm/pull/30408
* @lashahub made their first contribution in https://github.com/vllm-project/vllm/pull/30539
* @LuminolT made their first contribution in https://github.com/vllm-project/vllm/pull/29163
* @majiayu000 made their first contribution in https://github.com/vllm-project/vllm/pull/30615
* @MaoJianwei made their first contribution in https://github.com/vllm-project/vllm/pull/29797
* @Mercykid-bash made their first contribution in https://github.com/vllm-project/vllm/pull/26471
* @mgehre-amd made their first contribution in https://github.com/vllm-project/vllm/pull/30364
* @mivehk made their first contribution in https://github.com/vllm-project/vllm/pull/30512
* @mondaylord made their first contribution in https://github.com/vllm-project/vllm/pull/30671
* @noa-neria made their first contribution in https://github.com/vllm-project/vllm/pull/29320
* @PatrykSaffer made their first contribution in https://github.com/vllm-project/vllm/pull/30330
* @Peng-YM made their first contribution in https://github.com/vllm-project/vllm/pull/29074
* @realliujiaxu made their first contribution in https://github.com/vllm-project/vllm/pull/30059
* @redwrasse made their first contribution in https://github.com/vllm-project/vllm/pull/29261
* @Ri0S made their first contribution in https://github.com/vllm-project/vllm/pull/30532
* @sarathc-cerebras made their first contribution in https://github.com/vllm-project/vllm/pull/30188
* @scratch-ml made their first contribution in https://github.com/vllm-project/vllm/pull/30351
* @seokhyunan made their first contribution in https://github.com/vllm-project/vllm/pull/30648
* @shaharmor98 made their first contribution in https://github.com/vllm-project/vllm/pull/30203
* @taoyun951753 made their first contribution in https://github.com/vllm-project/vllm/pull/30037
* @tom-zju made their first contribution in https://github.com/vllm-project/vllm/pull/30057
* @tomtomjhj made their first contribution in https://github.com/vllm-project/vllm/pull/29692
* @vkuzo made their first contribution in https://github.com/vllm-project/vllm/pull/29196
* @vladnosiv made their first contribution in https://github.com/vllm-project/vllm/pull/30490
* @weiguihua2 made their first contribution in https://github.com/vllm-project/vllm/pull/30042
* @wenqiglantz made their first contribution in https://github.com/vllm-project/vllm/pull/30649
* @wkcn made their first contribution in https://github.com/vllm-project/vllm/pull/29879
* @wu-kan made their first contribution in https://github.com/vllm-project/vllm/pull/21804
* @wz1qqx made their first contribution in https://github.com/vllm-project/vllm/pull/30376
* @xyDong0223 made their first contribution in https://github.com/vllm-project/vllm/pull/30455
* @yifant-code made their first contribution in https://github.com/vllm-project/vllm/pull/30213
* @yjc9696 made their first contribution in https://github.com/vllm-project/vllm/pull/30040
* @yurekami made their first contribution in https://github.com/vllm-project/vllm/pull/30552
* @yuttian1 made their first contribution in https://github.com/vllm-project/vllm/pull/30102
* @ZhijianJiang made their first contribution in https://github.com/vllm-project/vllm/pull/30219
* @ZhiweiYan-96 made their first contribution in https://github.com/vllm-project/vllm/pull/29773

**Full Changelog**: https://github.com/vllm-project/vllm/compare/v0.12.0...v0.13.0