LLM Quantization Explained: GPTQ, AWQ, GGUF, and When Each One Wins
Serving a 7B model in fp16 takes roughly 14 GB of VRAM. A 70B model takes around 140 GB —
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Tag: vLLM
Speculative Decoding: Getting 2-3x More Out of Every GPU Pass
Watch a GPU while a large language model generates text and you’ll see something strange: for most of every forward
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vLLM Prefix Caching: The Prefill Optimization You’re Already Running
Every request that hits an LLM server pays the same tax before the first generated token appears: the prompt must
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Speculative Decoding in vLLM: Draft, Verify, and Cut Latency Without Losing the Distribution
Large language model inference has an awkward performance profile: the GPU does enormous math, then waits. Every token requires a