Best local LLM for Apple silicon

Short answer: Mixtral 8x7B Instruct at Q4_K_M for general use — 30.1 GB of Apple silicon, leaving 5.9 GB. Also: Seed-OSS 36B Instruct (Coding), Qwen3-VL 30B-A3B Instruct (Images).

“Best” here means the largest model in its category that clears the card with headroom left, at the highest-quality quant that still fits. This index measures memory and published perplexity, not task quality — it cannot tell you which model answers better, and does not pretend to.

The picks

For	Model	Quant	Est. VRAM	Headroom	Loss	Why
General	Mixtral 8x7B Instruct47B MoE	Q4_K_M	30.1 GB	5.9 GB	2.8%	The largest model of any kind that fits this budget. At Q4_K_M it gives up 2.8% perplexity against FP16. Only at 4K context — a longer window does not fit.
Coding	Seed-OSS 36B Instruct36B	Q5_K_M	27.8 GB	8.2 GB	1.3%	The largest in its category that still leaves headroom. At Q5_K_M it gives up 1.3% perplexity against FP16. Past 16K context the KV cache pushes it over.
Images	Qwen3-VL 30B-A3B Instruct30B-A3B	Q4_K_M	19.7 GB	16.3 GB	2.6%	The largest in its category that still leaves headroom. At Q4_K_M it gives up 2.6% perplexity against FP16. It still fits at 32K context, which is what the headroom is for.

Sized at 4K context with a 10% activation buffer; 66 of 81 models in the index clear this budget comfortably. Loss is the quantizer’s published perplexity figure against FP16, or a dash where none was published.

Cards this applies to

What fits is decided by capacity alone, so every card here returns the same list. What differs is how fast it reads the weights — each card’s page states its bandwidth and what that costs you.

What does not fit

The nearest miss is GLM-4.5-Air. Its smallest build in this index, Q2_K, needs about 37.2 GB — 1.2 GB over Apple silicon, before any context beyond 4K. Offloading to system RAM will load it at a speed set by your RAM, which is a different machine rather than a smaller quant.

Pushing context

At 4K context 66 models clear this budget. At 16K it is 66, and at 32K it is 66. The weights do not change — the KV cache is what grows.

Check your exact context length

By unified memory

Apple silicon spans a wider range than any GPU tier, so one figure would not describe it. The largest model each configuration clears, and how many it runs in total — remember macOS keeps part of the pool and caps what one process may wire down, so budget below nameplate.

Memory	Largest that fits	Models
16 GB	Mistral Small 24B Instruct · 13.2 GB	52
24 GB	Seed-OSS 36B Instruct · 19.9 GB	65
36 GB	Mixtral 8x7B Instruct · 30.1 GB	66
48 GB	GLM-4.5-Air · 37.2 GB	71
64 GB	Llama 4 Scout 17B (16E) · 55.9 GB	73
128 GB	DBRX Instruct · 84.3 GB	75

Common questions

What is the best local LLM for Apple silicon in 2026?

Mixtral 8x7B Instruct at Q4_K_M for general use — 30.1 GB of 36 GB, leaving 5.9 GB. Seed-OSS 36B Instruct if you want code, Qwen3-VL 30B-A3B Instruct if you want images. "Best" here means the largest model in its category that clears the card with headroom — this index measures memory and published perplexity, not task quality, so it cannot rank models on how well they answer.

How many models fit Apple silicon?

66 of the 81 models in this index clear it comfortably at 4K context — comfortably meaning the estimate uses at most 88% of the memory, so there is margin for the display and the runtime. At 16K that falls to 66, and at 32K to 66: the KV cache is what moves, not the weights.

What will not run on Apple silicon?

The nearest miss is GLM-4.5-Air: its smallest build here, Q2_K, needs about 37.2 GB — 1.2 GB over, before any context beyond 4K. Offloading part of it to system RAM will load it, at a speed set by your RAM rather than your chip; that is a different machine, not a smaller quant.

Do all Apple silicon cards run the same models?

They hold the same models — what fits is decided by capacity alone, so all 18 cards at this tier return this same list. What differs is how fast they read the weights: token generation reads the whole weight set once per token, so memory bandwidth sets the ceiling, and two cards with identical memory can be more than 2.5× apart on it. Each card's own page states its bandwidth and what that implies.