Model Runner V2

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 v0.25.0 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.

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.

vLLM v0.17.0 introduces a major upgrade to PyTorch 2.10, integrates FlashAttention 4, and significantly matures Model Runner V2 with features like Pipeline Parallelism. This release also adds full support for the Qwen3.5 model family and introduces new performance tuning flags.

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.