Best local LLM for Apple silicon

Short answer: Nemotron 3.5 Bolt 74B-A9B at IQ2_XXS for general use — 23.1 GB of Apple silicon, leaving 3.9 GB. Also: .

“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 · Coding · Images	Nemotron 3.5 Bolt 74B-A9B74B-A9B	IQ2_XXS	23.1 GB	3.9 GB	—	The largest model of any kind that fits this budget. Its quantizer published no perplexity figure, so none is quoted. It still fits at 32K context, which is what the headroom is for.Download: unsloth/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-GGUF · NVIDIA-Nemotron-3.5-Bolt-74B-A9B-IQ2_XXS.gguf · 22.02 GB · + vision projector 1.73 GB, separate · listing read 2026-10-16

Sized at 4K context with a 10% activation buffer; 97 of 125 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 Mixtral 8x7B Instruct. Its smallest build in this index, Q4_K_M, needs about 30.1 GB — 3.1 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 97 models clear this budget. At 16K it is 93, and at 32K it is 85. 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	DeepSeek-Coder-V2-Lite Instruct · 10.1 GB	62
24 GB	Qwen3-VL 30B-A3B Instruct · 15.8 GB	72
36 GB	Nemotron 3.5 Bolt 74B-A9B · 23.1 GB	97
48 GB	Nemotron 3.5 Bolt 74B-A9B · 23.1 GB	100
64 GB	GLM-4.5-Air · 37.2 GB	102
128 GB	DBRX Instruct · 84.3 GB	113

Common questions

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

Nemotron 3.5 Bolt 74B-A9B at IQ2_XXS for general use — 23.1 GB of 36 GB, leaving 3.9 GB. . "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?

97 of the 125 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 93, and at 32K to 85: the KV cache is what moves, not the weights.

What will not run on Apple silicon?

The nearest miss is Mixtral 8x7B Instruct: its smallest build here, Q4_K_M, needs about 30.1 GB — 3.1 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 24 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.