Inference Serving with vLLM: Continuous Batching, KV-Cache, and Speculative Decoding

Past ~$50K/month of LLM spend, hosted-API economics start to lose to self-hosted on the right hardware — but only if you understand the three primitives that distinguish modern inference servers from naive HTTP-front-of-model setups: continuous batching (the throughput multiplier), paged KV-cache (the concurrency multiplier), and speculative decoding (the latency multiplier). Senior engineers who can defend an inference architecture in numbers — "we get 1200 tokens/sec on one H100 with vLLM at 6

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