Thurmon Demich

Posted on Jul 22 • Originally published at bestgpuforllm.com

Best GPU for MLX in 2026: Apple Silicon Ranked for Local LLM

#mlx #applesilicon #m4 #ollama

Cross-posted from Best GPU for LLM — visit the original for our VRAM calculator, GPU comparison table, and current Amazon pricing.

Quick answer: For most people running LLMs on a Mac in 2026, an M4 Pro with 24-48GB of unified memory is the sweet spot. It handles every 7B and 13B model comfortably, plus low-quant 34B runs, and thanks to Ollama 0.30.8's new first-class MLX engine (shipped late June 2026) the tok/s numbers finally look serious next to a mid-range NVIDIA card. If you already know you want to run 70B locally, jump straight to an M4 Max 128GB — I'll explain why below.

Who this guide is for

I wrote this for the Mac user who is staring at a build page trying to decide between an M4 base, M4 Pro, or M4 Max — with the LLM use case as the tiebreaker. It is also for the NVIDIA-curious Mac owner who wants to know whether upgrading to an M4 Max is a real alternative to buying a discrete GPU tower. If you have not narrowed the platform question yet, my Mac vs NVIDIA for LLM head-to-head covers the broader tradeoffs; this piece assumes you have already decided Apple Silicon is on the shortlist.

MLX vs CUDA vs Metal (and why Ollama 0.30.8 matters)

MLX is Apple's array framework, designed from the ground up for Apple Silicon's unified memory. Metal is the graphics API underneath; MLX sits above it and gives LLM inference a purpose-built path that avoids the CPU-GPU copy overhead that llama.cpp's Metal backend has always had.

Ollama 0.30.8 (late June 2026) is the release that made MLX actually matter to non-hobbyists. Before that, using MLX meant fiddling with mlx-lm on the command line or leaning on LM Studio's MLX backend. Now Ollama detects Apple Silicon, pulls the MLX-quantized weights when they exist, and runs them natively. Together with MLX 0.31.x, Apple is claiming a ~4x TTFT improvement on M5 Neural Accelerators and roughly 20-30% steady-state gains on M4 chips versus the old llama.cpp Metal path.

If you already use Ollama, the upgrade is transparent — my best GPU for Ollama guide covers the NVIDIA side of that same release. If you prefer a GUI, LM Studio has had MLX support for months and remains the smoothest way in.

The Mac tier table (RAM per model size)

Here is what actually runs at usable speed today, based on the MLX 0.31.x + Ollama 0.30.8 stack. Q4 numbers assume MLX-quantized weights; expect 15-25% slower on Metal-only paths.

Mac SKU	Unified RAM	7B Q4	13B Q4	34B Q4	70B Q4
M3 Pro 18GB	18GB	~28 tok/s	tight, ~15 tok/s	won't fit	won't fit
M4 base 16GB	16GB	~35 tok/s	tight, low context	won't fit	won't fit
M4 Pro 24GB	24GB	~45 tok/s	~24 tok/s	won't fit	won't fit
M4 Pro 48GB	48GB	~45 tok/s	~28 tok/s	~14 tok/s (tight)	won't fit
M4 Max 64GB	64GB	~50 tok/s	~32 tok/s	~22 tok/s	won't fit
M4 Max 128GB	128GB	~50 tok/s	~32 tok/s	~22 tok/s	~11 tok/s
M4 Ultra 256GB	256GB	~55 tok/s	~35 tok/s	~26 tok/s	~14 tok/s (+405B Q4 at ~5-7 tok/s)

For reference, an RTX 4090 24GB hits roughly 65 tok/s on 7B Q4 and 40 tok/s on 13B Q4 — still faster on pure speed than any Mac in the lineup. The Mac wins on unified memory ceiling (nothing on the consumer NVIDIA side can touch 128GB, let alone 256GB, without going multi-GPU) and on power draw. That is the whole trade.

For the underlying model-to-VRAM math — which applies here because MLX is memory-bound just like CUDA — see how much VRAM for local LLM.

Which Mac should YOU buy?

You mostly run 7B-8B models (Llama 3.1 8B, Mistral 7B, Gemma 4 E4B) — the M4 Pro 24GB is enough and honestly a bit of overkill. If budget is tight, the M4 base 16GB works but you will feel context-length pressure inside a month. I already covered the mini-form-factor angle in can the Mac mini run an LLM.
You want 13B comfortably with a real 8K-16K context — go M4 Pro 48GB. The extra headroom is what lets you keep two apps open plus the model. This is the config I recommend most often to friends.
You want to run 34B locally (Qwen 3.6 32B, DeepSeek Coder 33B) — you need the M4 Max 64GB. Don't try to squeeze 34B onto a 48GB Pro; you will spend the savings on frustration.
You want 70B on your desk — M4 Max 128GB is the answer, and at roughly 11 tok/s it lands in the same ballpark as a dual RTX 3090 rig for a lot less noise and heat.
You are running production or a home lab with 400B+ MoE models — that's M4 Ultra 256GB territory (or a Mac Studio). Nothing else consumer-priced touches it.
You want the option to serve requests — Mac's unified memory holds up better than you'd expect, but for concurrency I'd still lean NVIDIA. Not because MLX is bad, but because the CUDA ecosystem around serving is more mature.

Contrarian take: MLX is legit, but CUDA still wins on raw speed

Here is the take I keep repeating to Mac-loyalist friends who assume MLX + Ollama 0.30.8 closed the gap: it didn't. It narrowed it. A tuned 4090 rig still beats an M4 Max on tok/s at 7B and 13B by a comfortable margin, and if you are doing anything speed-sensitive — code completion inline in an editor, agent loops, batch summarization — that gap is a real productivity hit. What MLX buys you is unified memory ceiling (128GB and 256GB are Apple-only at consumer prices), power efficiency (an M4 Max sips 40-60W under load), and a laptop form factor. Buy the Mac for those. Do not buy it because you think MLX beats CUDA on tok/s. It doesn't, and pretending it does will just leave you disappointed.

Common mistakes to avoid

Buying an M4 base 16GB thinking you'll grow into 34B — you won't. Unified memory is not upgradeable. Pick the RAM tier you actually need in 12 months, not the one you can afford this weekend.
Assuming the Metal path when MLX is available — this bit a lot of people pre-Ollama 0.30.8. Check ollama ps or LM Studio's runtime badge; if it says llama.cpp/Metal instead of MLX, you're leaving 20-30% on the table.
Ignoring that MLX-quantized weights are a separate download — the ecosystem is still catching up. Popular models (Llama, Qwen, Gemma, Mistral) have MLX quants; niche fine-tunes often don't yet.
Comparing Mac tok/s to CUDA tok/s in isolation — the honest comparison is tok/s per watt or tok/s per dollar including the whole system. On both, the M4 Max holds up better than the raw numbers suggest.

Final verdict

If you want to run...	Buy this Mac	Realistic tok/s
7B Q4 casually	M4 Pro 24GB	~45
13B Q4 with real context	M4 Pro 48GB	~28
34B Q4	M4 Max 64GB	~22
70B Q4	M4 Max 128GB	~11
400B+ MoE	M4 Ultra 256GB	~5-7
Pure speed at 7B/13B	RTX 4090 instead	~65 / ~40

If you're already on Apple Silicon, MLX + Ollama 0.30.8 is the free upgrade that finally makes local LLM on a Mac feel unembarrassing.

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