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ai deep-dive Self-Hosted Inference

vLLM: The Default Choice for Self-Hosted Inference — and When It's Over-Engineering

vLLM is the de facto standard for self-hosted LLM inference (89,470 GitHub stars, verified 2026-08-21), built on managing the KV cache the way an OS manages paged memory. But the selection question isn't how fast it is — it's your GPU utilization. Using Red Hat's measured 793 output tokens/second, a fully saturated A100 costs roughly $0.70 per million output tokens; at 10% utilization that becomes $7, more than most cloud APIs.

#vllm#llm-inference#self-hosting#gpu#pagedattention#cost
ai guide Self-Hosted Inference

vLLM — From PagedAttention to a Production-Grade LLM Inference Engine

vLLM uses PagedAttention to eliminate KV cache memory waste, combining continuous batching and prefix caching to become the most widely adopted open-source LLM inference engine today.

#vllm#llm-inference#pagedattention#model-serving#gpu