What is the best local LLM for a Mac Studio M3 Ultra?

With its huge unified memory, the M3 Ultra runs Llama 3.3 70B at Q8 (near-full quality) and 100B+ MoE models like gpt-oss 120B locally at ~25-30 tokens/sec. For OpenClaw you can even keep several models resident at once for multi-agent setups.

How much unified memory does the M3 Ultra have?

The Mac Studio M3 Ultra configures up to 512 GB of unified memory at roughly 800 GB/s bandwidth. That is enough for 70B models at Q8, large mixture-of-experts models, and very long context — workloads no single consumer GPU can hold.

Is the M3 Ultra fast for local LLMs?

For its class, yes. The ~800 GB/s bandwidth delivers ~25-30 tokens/sec on 70B-class models — much faster than an M4 Pro and enough for comfortable interactive use, though still below datacenter GPUs on raw throughput.

M3 Ultra vs M4 Max for local AI?

The M3 Ultra wins on memory ceiling (up to 512 GB vs 128 GB) and bandwidth (~800 vs ~410 GB/s), so it runs bigger models faster. The M4 Max is cheaper and plenty for 70B at Q4; choose the Ultra when you need 70B at Q8, big MoE models, or multiple resident models.

← All guides

Hardware July 7, 2026

Best Local LLM for Mac Studio M3 Ultra (2026): Up to 512GB

The Mac Studio M3 Ultra scales to 512 GB of unified memory at ~800 GB/s. That combination puts models on your desk that no single consumer GPU can touch — the constraint stops being 'what fits' and becomes 'how fast'.

Picking hardware for an OpenClaw host?

Use the local model calculator first, then see our AI training options if you want help matching your workload to the right rig.

Short answer: the M3 Ultra is the top consumer local-LLM machine for sheer model size. With 256-512 GB of unified memory it runs Llama 3.3 70B at Q8, 100B+ MoE models (gpt-oss 120B, Mistral Small MoE), and very long context — all locally at ~25-30 tok/sec. The ~800 GB/s bandwidth makes it noticeably faster than an M4 Max for big models.

The Memory Math

The M3 Ultra is the flex machine: 512 GB of unified memory means 70B at Q8, big MoE models, and enough room to keep several models loaded for multi-agent OpenClaw setups — silently, on one desktop. If you need the absolute largest local models without a multi-GPU server, this is it.

What Actually Fits (Model Picks)

Model	Quant	Memory	Speed	Notes
Llama 3.3 70B	Q8_0	~75 GB	~25 tok/s	70B at near-full quality
gpt-oss 120B (MoE)	Q4/Q5	~70-90 GB	~28 tok/s	Flagship MoE, runs locally
Qwen 3.6 27B	Q8_0 + huge ctx	~30 GB	~40 tok/s	27B with massive context
Multiple models resident	mixed	fits easily	—	Run several at once for agents

What You Can’t Run

Beating a datacenter GPU on raw throughput — Apple bandwidth is high for a desktop but below an H100-class card; expect ~25-30 tok/s, not hundreds.
Training large models — this is an inference machine, not a training rig.
Cheap — the value case is “runs models a $30k GPU box would,” not low price.

🎮 THE BIG-MEMORY APPLE SILICON PICK

The M3 Ultra Mac Studio configures to 512 GB — the largest local-model machine you can put on a desk. A 48 GB+ Mac is the entry to 70B; the Mac mini M4 is the budget always-on host for smaller models.

48GB+Premium Mac 48 GB+ ↗ MINIMac mini M4 ↗

OpenClaw Setup

Point OpenClaw at your local model through Ollama:

# pull and run your pick, then set it as the OpenClaw default
ollama pull llama3.3:70b
openclaw config set agents.defaults.models.chat "ollama/llama3.3:70b"

For agent reliability, prefer a model with clean tool-call output (gpt-oss 20B where it fits) and cap context to what your memory holds. See the tool-calling reliability guide.