vLLM
### TL;DR
vLLM is a high-throughput, memory-efficient inference and serving engine for large language models. The v0.25.0 release introduces Model Runner V2 as the default engine for dense models, removes legacy PagedAttention, and adds extensive support for new models, hardware, and performance optimizations. Key technical advancements include improved support for heterogeneous speculative decoding, a new streaming parser engine, and enhanced support for a wide array of architectures such as GLM-5, MiniMax-M3, and various multimodal models.
Key Insights & Metrics
Key Features
- Model Runner V2 as the default engine for dense models
- Universal speculative decoding for heterogeneous vocabularies
- New Streaming Parser Engine for tool-call and reasoning frameworks
- Extensive hardware-specific performance optimizations for NVIDIA Blackwell, AMD ROCm, and Intel XPU
- Removal of legacy PagedAttention in favor of V1/MRv2 backends
- Enhanced quantization support, including NVFP4, FP8 MoE, and Humming weight-only inference
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