About
The Mac-specific layer is the point.
Local-LLM advice is often written around discrete GPUs. Mac hardware coverage is often written without serious attention to inference. MacLocalLLM works at the intersection.
Editorial promise
A narrower promise, kept.
We cover MLX, llama.cpp, Ollama, LM Studio, quantization, unified-memory sizing and Apple silicon hardware tradeoffs — when they materially change what runs, how fast, and how well.
What that rules out
No benchmark theatre, no copied spec sheets, no results we did not run ourselves. When the local Mac loses, we publish that too — the first result on this site is one where it did.
What we cover
Three decisions that change whether a model actually fits.
Mac local AI has its own constraints and advantages. We focus on the decisions that change whether a model fits, runs well, and helps with real work.
Hardware
Choose the Mac
Chips, memory bandwidth, unified memory, thermals, and when an upgrade changes the result.
GuidesRuntime
Choose the runner
Where MLX, llama.cpp, Ollama and LM Studio fit, without treating one tool as the answer to every job.
GuidesModel fit
Choose what fits
Parameter count, quantization, context, memory headroom, speed and output quality, reasoned about together.
BenchmarksHow we test
Numbers need context.
Every result carries the machine, model, quantization, runtime and settings, so you can reproduce it or argue with it.
01
Name the machine
Chip, GPU configuration, unified memory, storage, OS and power mode.
02
Name the workload
Model, quantization, context, prompt, output length, runtime and settings.
03
Show the tradeoff
Speed, memory pressure, quality, thermals, stability and actual task usefulness.
Who writes this
Written and measured by ikeo1.
Published by IKEO Group. Questions and corrections belong on r/MacLocalLLM, where the community lives.