Deep Dive into LLM Inference Mechanics
Normal-Tangelo-7120 · reddit · 2026-07-12
This is part 4 of an LLM fundamentals series for software engineers, focusing on what happens inside the model between hitting enter and seeing the first token.
The article primarily explains:
- How models generate token by token and why this introduces latency
- The role of the KV cache and how it mitigates inference costs
- How decoding strategies like temperature, top-k, top-p affect the output
The author also provides links to the first 3 parts of the series, covering basics like tokenization, loss/backprop, and scaling/fine-tuning/RLHF/DPO.
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