vLLM releases v0.32.0
Adds structured decisions, generic parsers and broader low-precision KV caches, with model- and hardware-specific requirements.
vLLM ↗Product profile →TOOLORA / Local reading edition
vLLM
Serve language models with KV-cache management, continuous batching, quantization and multi-device parallelism.
Overview
vLLM is an open-source engine for language-model inference and serving. Configure a model and hardware to generate in batches or run an API server for application requests. PagedAttention, continuous batching and prefix caching manage inference memory and scheduling; quantization, structured outputs and distributed inference are also supported. It does not supply model weights or hosted compute. Compatibility and performance depend on the model, backend and configuration.
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Official sources and references
- Official websitehttps://docs.vllm.ai/en/latest/
Descriptions summarize public information; consult official websites for features, pricing and regional availability.