Best Local AI Models for Coding
Running a coding assistant locally gives you zero-latency completions, full privacy for proprietary code, and no API costs. The best coding models for local use combine strong code generation with fast inference on Apple Silicon. Here are the top picks across all hardware configurations.
What local models change for coding
A coding assistant on your own machine changes the economics of everyday development. There is no per-token bill for autocomplete, refactors, or the tenth rubber-duck question of the hour. Completions also arrive at local speed, so the tool stays out of your way instead of breaking your flow.
The stronger argument for many teams is privacy. Source code is the asset competitors want most, and every cloud prompt ships a slice of it to someone else. A local model keeps proprietary logic, config files, and unreleased product code on hardware you control. For client work under NDA, that is often the difference between allowed and forbidden. It also ends the procurement debate, because there is no vendor to vet.
Current local coding models handle completion, explanation, test writing, and review. Pair one with Continue.dev, Cline, or aider and it slots into the IDE you already use. The picks below run well on ordinary MacBooks, and larger rigs unlock the 27B to 35B tier that approaches cloud quality.
Choose Your Device
Get coding model recommendations tailored to your specific hardware.
Top Coding Models (All Hardware)
Six models lead the coding ranking right now. The Q8 builds win on raw quality; the Q4 builds trade a little of it for a much smaller memory footprint. Start from your RAM budget, then take the strongest row that fits.
How We Picked These Models
Every pick on this page comes from the ModelFit recommendation engine, not a hand-written list. We filter the model dataset for coding-tagged entries, drop cloud-only models, and rank what remains on quality and popularity scores. RAM figures use the same memory-budget rule as the ModelFit wizard, so a model only appears here if the engine would recommend it for a real machine. Speed and quality scores are planning estimates, not measured benchmarks. The ranking rebuilds from the dataset on every deploy, so this page stays in sync with every device page on the site.
How to read the table: Min RAM is the smallest machine that runs the model, and Load is the memory the weights occupy at runtime before any context. Quality is a ModelFit score on a 0-100 scale, derived from publisher evaluations and real-world adoption. Treat every number here as a planning estimate, and run the wizard for figures tuned to your exact chip and RAM.