Step-by-Step Guide to Becoming an Inference Optimization Engineer, From Quantization to Speculative Decoding
ashishllm · x · 2026-09-14
Aimed at the rising US demand for inference engineers who can serve LLMs, VLMs and STT/TTS to millions of users, this step-by-step guide covers: quantization (precision tradeoffs and GPU requirements), Paged Attention and KV cache mechanics, Flash Attention and kernel fusion, continuous batching with vLLM, prompt caching, speculative decoding with a small draft model, and streaming responses.
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