MiniMax-M3

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.

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.

v0.24.0 introduces extensive support and performance optimizations for new models like MiniMax-M3 and DeepSeek-V4, matures the Model Runner V2 with default quantization support, and overhauls device selection by removing internal use of CUDA_VISIBLE_DEVICES.

This release introduces major support for DiffusionGemma and faster Gemma 4 via MTP, alongside significant experimental features like a Model Hub and Chat with Files (RAG) in Unsloth Studio. It also updates the underlying llama.cpp backend for improved hardware compatibility and stability.