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High-throughput inference engine
vLLM
Production-grade LLM serving with PagedAttention — now expanding to consumer devices.
Official siteAxes (0–100)
- Local control / custody82
- Open stack (models & tooling)88
- Regulatory posture (curated)42
- Interoperability75
Last reviewed: 2026-04-10
Facts (curated)
- Focus
- High-throughput, memory-efficient LLM inference and serving. PagedAttention, continuous batching, speculative decoding.
- Consumer use
- Apple Silicon via vllm-metal plugin (MLX backend). Consumer GPU use via on-demand gateway pattern. Primarily a server-side tool.
- Backing
- Originated at UC Berkeley Sky Lab. Community-driven with broad industry sponsorship. Apache 2.0 licensed. 75k GitHub stars.
Pros
Highest throughput for GPU serving. Industry standard for production LLM deployments. Broad model and hardware support.
Cons / risks
Primarily designed for production GPU servers — complex setup, Linux-first. Consumer-device use is secondary and experimental.
Related links
- vLLM Metal plugin — Apple Silicon via MLX
GitHub · 2026-04-06
- vLLM — high-throughput LLM serving
vLLM · 2026-04-03