FlashInfer

vLLM v0.27.0 introduces Kimi K3 support, new model additions like Qwen3.5 and VaultGemma, and a significant upgrade to PyTorch 2.13.0. The release also enhances performance and features across various areas including FlashAttention 4, Model Runner V2, KV offloading, and hardware enablement.

vLLM v0.26.0 introduces the Inkling model family, significant performance boosts for DeepSeek-V4, flexible attention backends, and enhanced KV offloading. The release also includes a Rust frontend with multimodal capabilities and deeper integration with Transformers 5.13.0.

v0.25.1 is a patch release that fixes two critical bugs. The first prevents model launching from being blocked when FFmpeg is not available for TorchCodec, and the second guards mixed-dtype allreduce RMSNorm quant fusions to prevent corrupted outputs.

vLLM v0.25.0 introduces Model Runner V2 as the default for dense models, significantly improving performance and adding support for new features like EVS and realtime embeddings. The release also deprecates PagedAttention and enhances the Transformers backend to match native vLLM speed, alongside numerous model additions and performance optimizations across various hardware platforms.

This release focuses heavily on DeepSeek V4 maturity with new kernel support and packaging, significant advancements in Model Runner V2, and the introduction of an experimental Rust frontend. Performance saw notable gains from batch-invariant inference with Cutlass FP8 and the rollout of multi-tier KV cache offloading.

vLLM v0.20.1 is a patch release focused on stabilizing and improving performance for DeepSeek V4, including various kernel optimizations and critical bug fixes across CUDA and ROCm platforms.

v0.18.0 introduces major features like gRPC serving, GPU-less render serving, and significant improvements to KV cache offloading and Elastic Expert Parallelism. Ray is now an optional dependency, and numerous model-specific fixes and kernel optimizations have been integrated.

vLLM v0.16.0 introduces full support for Async scheduling with Pipeline Parallelism, a new Realtime WebSocket API, and a major overhaul of XPU platform support by deprecating IPEX in favor of vllm-xpu-kernels. This release also includes extensive model support additions and performance optimizations across various hardware platforms.

This release marks the complete transition to the V1 engine, removing all V0 components while introducing support for DeepSeek-V3.2 and Qwen3 architectures. It features significant performance optimizations including KV cache CPU offloading, DeepGEMM by default, and Dual-Batch Overlap.

v0.10.1 introduces support for Blackwell and RTX 5090 GPUs, expands vision-language model compatibility, and adds a plugin system for model loaders. It also deprecates V0 FA3 support and removes AQLM quantization.

vLLM v0.7.0 upgrades to PyTorch 2.7 and CUDA 12.8, introducing initial NVIDIA Blackwell support and advanced scaling features like Expert and Data Parallelism. It also includes significant model expansions, a migration to MkDocs, and a shift to deterministic defaults for the V1 engine.

This release introduces Day 0 support for Qwen3, structural tag tool calling via xgrammar, and disaggregated serving via the KV Connector API. It includes significant performance optimizations for MoE kernels and breaking changes to CLI argument formatting for chunked prefill and multi-step outputs.