LLM config tuning thread weighs KV cache tradeoffs in an 80K-context Laguna setup
kingo86 · reddit · 2026-07-26
The post asks how people test and optimize LLM model configs/params and describes a workflow built around a headless LLM machine, llama.cpp router mode, models.ini, and repo docs that are “AI manicured.”
The attached screenshot adds concrete tuning details from a Laguna S2.1 setup:
- Switching KV cache from q80 to F16 improves throughput materially.
- The screenshot claims q8 KV is worse: decode drops from about 40.8 to 36.6 tok/s, prefill from 873 to 685 tok/s, while saving only about 1.5 GiB RSS at that context.
- The user plans to restore F16 KV and run a 50K-token retrieval test to validate the 80K context configuration under realistic pressure.
Overall it is a practical discussion of config management, benchmarking, and long-context deployment tradeoffs.
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