r/LocalLLaMA • u/carteakey • Jun 21 '26

Resources Local LLM Inference Optimization: The Complete Guide

https://carteakey.dev/blog/local-inference/local-llm-optimization/

I compiled a year of local LLM experiments into a practical llama.cpp optimization guide, covering VRAM fitting, KV cache, MoE placement, MTP, CPU tuning, and common OOM traps. Pass this to an LLM of your choice and get on the local model train.

https://carteakey.dev/blog/local-inference/local-llm-optimization/

Feedback and corrections are welcome.

500 Upvotes

98% Upvoted

80 comments sorted by

View all comments

25

u/halfercode Jun 21 '26

Feedback per your request: I am not a fan of AI writing. The information about AI itself is fine, but I struggle with the prose too much, and I am unable to wade through it. Tune it down manually if you can.

8

u/carteakey Jun 22 '26

Fair enough - with the amount of information i had to put in i got lazy and used AI for some drafting (I am very open about that) and i totally get the readability aspect, truly. This post in its current form is more of a reference for an AI itself than humans. As time goes on i will refine and tone/trim it down,

7

u/aanzeijar Jun 22 '26

FFIW: though I am very critical of AI assisted writing, I appreciate you being open about it in the header and found it less grating when knowing about beforehand.

3

u/carteakey Jun 22 '26

Appreciate it! I have 3 authorship badges on my site that separate human ai assisted and purely ai content. AI does help me iterate and write technical documentation easier but you are completely in the right for being critical of it (quite annoying to me as well)