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feat(deps): update dependency vllm-charts ( v0.14.1 → v0.15.0 ) #1663

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feat(deps): update dependency vllm-charts ( v0.14.1 → v0.15.0 ) #1663

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@chaplain-grimaldus chaplain-grimaldus bot commented Jan 29, 2026 •
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This PR contains the following updates:

Package Update Change
vllm-charts minor v0.14.1 → v0.15.0

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Some dependencies could not be looked up. Check the Dependency Dashboard for more information.

Release Notes

vllm-project/vllm (vllm-charts)

v0.15.0

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Highlights

This release features 335 commits from 158 contributors (39 new)!

Model Support
Engine Core
  • Async scheduling + Pipeline Parallelism: --async-scheduling now works with pipeline parallelism (#​32359).
  • Mamba prefix caching: Block-aligned prefix caching for Mamba/hybrid models with --enable-prefix-caching --mamba-cache-mode align. Achieves ~2x speedup by caching Mamba states directly (#​30877).
  • Session-based streaming input: New incremental input support for interactive workloads like ASR. Accepts async generators producing StreamingInput objects while maintaining KV cache alignment (#​28973).
  • Model Runner V2: VLM support (#​32546), architecture improvements.
  • LoRA: Inplace loading for memory efficiency (#​31326).
  • AOT compilation: torch.compile inductor artifacts support (#​25205).
  • Performance: KV cache offloading redundant load prevention (#​29087), FlashAttn attention/cache update separation (#​25954).
Hardware & Performance
NVIDIA
  • Blackwell defaults: FlashInfer MLA is now the default MLA backend on Blackwell, with TRTLLM as default prefill (#​32615).
  • MoE performance: 1.2-2% E2E throughput improvement via grouped topk kernel fusion (#​32058), NVFP4 small-batch decoding improvement (#​30885), faster cold start for MoEs with torch.compile (#​32805).
  • FP4 kernel optimization: Up to 65% faster FP4 quantization on Blackwell (SM100F) using 256-bit loads, ~4% E2E throughput improvement (#​32520).
  • Kernel improvements: topk_sigmoid kernel for MoE routing (#​31246), atomics reduce counting for SplitK skinny GEMMs (#​29843), fused cat+quant for FP8 KV cache in MLA (#​32950).
  • torch.compile: SiluAndMul and QuantFP8 CustomOp compilation (#​32806), Triton prefill attention performance (#​32403).
AMD ROCm
  • MoRI EP: High-performance all2all backend for Expert Parallel (#​28664).
  • Attention improvements: Shuffle KV cache layout and assembly paged attention kernel for AiterFlashAttentionBackend (#​29887).
  • FP4 support: MLA projection GEMMs with dynamic quantization (#​32238).
  • Consumer GPU support: Flash Attention Triton backend on RDNA3/RDNA4 (#​32944).
Other Platforms
  • TPU: Pipeline parallelism support (#​28506), backend option (#​32438).
  • Intel XPU: AgRsAll2AllManager for distributed communication (#​32654).
  • CPU: NUMA-aware acceleration for TP/DP inference on ARM (#​32792), PyTorch 2.10 (#​32869).
  • Whisper: torch.compile support (#​30385).
  • WSL: Platform compatibility fix for Windows Subsystem for Linux (#​32749).
Quantization
  • MXFP4: W4A16 support for compressed-tensors MoE models (#​32285).
  • Non-gated MoE: Quantization support with Marlin, NVFP4 CUTLASS, FP8, INT8, and compressed-tensors (#​32257).
  • Intel: Quantization Toolkit integration (#​31716).
  • FP8 KV cache: Per-tensor and per-attention-head quantization via llmcompressor (#​30141).
API & Frontend
  • Responses API: Partial message generation (#​32100), include_stop_str_in_output tuning (#​32383), prompt_cache_key support (#​32824).
  • OpenAI API: skip_special_tokens configuration (#​32345).
  • Score endpoint: Flexible input formats with data_1/data_2 and queries/documents (#​32577).
  • Render endpoints: New endpoints for prompt preprocessing (#​32473).
  • Whisper API: avg_logprob and compression_ratio in verbose_json segments (#​31059).
  • Security: FIPS 140-3 compliant hash option for enterprise/government users (#​32386), --ssl-ciphers CLI argument (#​30937).
  • UX improvements: Auto api_server_count based on dp_size (#​32525), wheel variant auto-detection during install (#​32948), custom profiler URI schemes (#​32393).
Dependencies
Breaking Changes & Deprecations
  • Metrics: Removed deprecated vllm:time_per_output_token_seconds metric - use vllm:inter_token_latency_seconds instead (#​32661).
  • Environment variables: Removed deprecated environment variables (#​32812).
  • Quantization: DeepSpeedFp8 removed (#​32679), RTN removed (#​32697), HQQ deprecated (#​32681).
Bug Fixes
  • Speculative decoding: Eagle draft_model_config fix (#​31753).
  • DeepSeek: DeepSeek-V3.1 + DeepGEMM incompatible scale shapes fix (#​32361).
  • Distributed: DP+MoE inference fix via CpuCommunicator (#​31867), P/D with non-MoE DP fix (#​33037).
  • EPLB: Possible deadlock fix (#​32418).
  • NIXL: UCX memory leak fix by exporting UCX_MEM_MMAP_HOOK_MODE=none (#​32181).
  • Structured output: Outlines byte fallback handling fix (#​31391).

New Contributors 🎉

Full Changelog: vllm-project/vllm@v0.14.1...v0.15.0

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