The Best Local AI Models for Apple Silicon (2026)

Over two hundred models run on Apple Silicon today, which is exactly the problem. These are the ones worth your disk space in 2026 — picked by job, not by leaderboard, by people who run them in CI every day.

"Best local model" is the wrong question until you attach a job to it. The best model for an autonomous coding agent is not the best model for an 8 GB MacBook Air, and neither is the best one for reading screenshots. So this guide is organized the way the decision actually happens: what are you trying to do, and how much unified memory do you have?

Two things make these picks different from a leaderboard scrape. First, every model here runs on Apple Silicon today, via MLX, with the exact command to serve it. Second, we don't just list them — the rapid-mlx release pipeline literally will not ship unless coding agents pass a live test against the top pick below, and five of these models went through our own MacBook Pro benchmark. These are working recommendations, not aggregated ones.

Every model below runs with one command after a one-line install:

rapid-mlx serve <alias>

Best all-rounder: Qwen 3.6 35B

qwen3.6-35b-4bit · 17.5 GB · runs well on 32 GB Macs

The default answer in 2026. It's a mixture-of-experts model — 35B parameters stored, only ~3B active per token — which means it decodes at interactive speeds while scoring like a much bigger dense model. Coding, tool calling, general chat: it's the family to beat in open weights, and the full Qwen-on-Mac guide covers every other size if 32 GB isn't your tier.

On 48 GB+ Macs, step up to qwen3.6-35b-8bit (35 GB) for a quality bump.

Best for coding agents: the same Qwen, at 8-bit

qwen3.6-35b-8bit · 35 GB · 48 GB+ Macs

This is the least hypothetical recommendation on the page: it's the exact model our release CI uses to gate every rapid-mlx release — Claude Code, Codex, Aider and Hermes have to complete real multi-step editing tasks against it before a version can ship. Multi-step agent work punishes quantization more than chat does; the 8-bit is where tool-call reliability gets boring, in the good way.

On 24–32 GB, qwen3.6-35b-4bit still agent-works well; there's also qwen3-coder-30b-4bit (15 GB), a coding-tuned MoE alternative. See the local coding agents guide for the full agent-side setup.

Best under 8 GB: LFM2.5, plus one absurdity

lfm2.5-2.6b-4bit · 1.6 GB · any Apple Silicon Mac

Liquid's LFM2.5 2.6B is our 8 GB-tier pick on the hardware guide: 1.6 GB on disk, quick, and surprisingly composed for its size. If you have a base MacBook Air, start here.

The absurdity: bonsai-27b-2bit (7.9 GB) — a 27-billion-parameter model ternarized down to ~2 bits per weight, small enough for an 8 GB machine. It placed second overall in our five-model benchmark, beating models that use twice the memory. It shouldn't work. It does.

Best reasoning: GPT-OSS

gpt-oss-20b · 10 GB · 16 GB+ Macs

OpenAI's open-weights family is reasoning-first: it thinks before it answers, streams that thinking separately (rapid-mlx demultiplexes it into reasoning_content on the OpenAI wire), and has strong tool use. The 20B runs on a 16 GB machine, which makes it the cheapest real reasoning you can host. With 48 GB+, gpt-oss-120b-mxfp4-q4 (35 GB) is the step up.

Best vision: Qwen3-VL

qwen3-vl-8b-4bit · 4.0 GB · 16 GB+ Macs

For "what's in this screenshot / chart / photo" work, Qwen3-VL is the practical local pick — the 8B fits modest Macs, and qwen3-vl-30b-4bit (15 GB, MoE) upgrades the quality on 32 GB machines. Vision-language models route through the same OpenAI-compatible endpoint, images and all.

Best if RAM is no object: the 100B+ class

qwen3.5-122b-mxfp4 · 61 GB · 64 GB+ Macs

A Mac Studio changes the question entirely. Qwen 3.5's 122B-A10B flagship in mxfp4 is the strongest general model most people can host at home, and because it's MoE (10B active), it stays responsive. Beyond that sits the DeepSeek V4 Flash class (90–167 GB depending on quant) for 128 GB+ Studios — the M3 Ultra benchmark covers what that tier actually feels like.

And Gemma, for the 16 GB middle

gemma-4-26b-4bit · 13 GB · 16–24 GB Macs

Google's Gemma 4 26B is the strongest thing that squeezes into the 16 GB tier, and its instruction-following style is noticeably different from Qwen's — some people prefer it for prose and summarization. Worth having as your second model.

The one-table version

Rule of thumb when substituting your own picks: weights should be at most ~2/3 of your RAM, because your context window and macOS need the rest. The memory deep-dive has measured numbers for 82 models if you want precision, and the by-RAM guide walks tier by tier.

Frequently asked questions

What is the best local AI model for Mac in 2026?

For most people with 32 GB of RAM: Qwen 3.6 35B at 4-bit — it combines near-flagship quality with mixture-of-experts speed. On 8–16 GB machines, LFM2.5 2.6B or GPT-OSS 20B are the strong picks; with 64 GB or more, Qwen 3.5's 122B flagship is the strongest model you can host at home.

How much RAM do I need to run AI models locally on Apple Silicon?

8 GB runs real models (2–8 GB of weights), 16 GB opens up the 10 GB class including GPT-OSS 20B, 32 GB comfortably runs the 17.5 GB Qwen 3.6 35B, and 64 GB+ hosts 60 GB-class flagships. A useful rule: model weights should stay under about two-thirds of your total unified memory so context and the OS have room.

Can local models on a Mac actually replace cloud AI?

For coding assistance, summarization, chat and tool-calling workflows — yes, routinely, and they're private and free per token. The hardest reasoning and very large contexts still favor frontier cloud models. Most people end up hybrid: local for the everyday loop, cloud for the rare hardest problems.

Which is better on Apple Silicon — MLX or llama.cpp/Ollama?

MLX is Apple's own array framework, designed for unified memory, and it's what rapid-mlx builds on. GGUF-based stacks like Ollama and llama.cpp also run on Macs and have broader non-Apple reach. If you're all-in on Apple Silicon, MLX-native serving is built for exactly your hardware — see our migration guide from Ollama for a practical comparison.