Can Llama 3.3 70B run on Mac Studio M3 Ultra 256GB?

YES — Runs Great

A79Great

Estimated from fit model

Llama 3.3 70B needs ~76.1 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~14 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 76.1 GB, 14.2 tok/s, Runs well

76.1 GB required184.3 GB available

41% VRAM used

Fit status

Runs well

Decode

14.2 tok/s

TTFT

13649 ms

Safe context

128K

Memory

76.1 GB / 184.3 GB

Memory breakdown

Weights42.7 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom27.6 GB

See how fast it feels

See how fast it feelsLlama 3.3 70B on Mac Studio M3 Ultra 256GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 14.2 tok/s decode · 13.6s TTFT (warm) · 36 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	14.2 tok/s	7445 ms	128K
Coding	A	Runs well	14.2 tok/s	13649 ms	128K
Agentic Coding	A	Runs well	14.2 tok/s	19854 ms	128K
Reasoning	A	Runs well	14.2 tok/s	16131 ms	128K
RAG	A	Runs well	14.2 tok/s	24817 ms	128K

Quantization options

How Llama 3.3 70B (70B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	A73
Q3_K_S	3	34.3 GB	Low	A74
NVFP4	4	39.2 GB	Medium	A74
Q4_K_M	4	42.7 GB	Medium	A75
Q5_K_M	5	50.4 GB	High	A76
Q6_K	6	57.4 GB	High	A77
Q8_0	8	74.9 GB	Very High	A79
F16Best for your GPU	16	143.5 GB	Maximum	A82

Get started

Copy-paste commands to run Llama 3.3 70B on your machine.

Run

ollama run llama3.3

Your hardware

More models your Mac Studio M3 Ultra 256GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	8.1 tok/s
Qwen 3.5 122B A10B	122B	S	34.7 tok/s
DeepSeek V4 Flash	284B	S	17.8 tok/s
Mistral Small 4 119B	119B	S	37.6 tok/s
GPT-OSS 120B	117B	S	8.5 tok/s

Frequently asked questions

Can Mac Studio M3 Ultra 256GB run Llama 3.3 70B?

Yes, Mac Studio M3 Ultra 256GB can run Llama 3.3 70B with a A grade (Runs well). Expected decode speed: 14.2 tok/s.

How much VRAM does Llama 3.3 70B need?

Llama 3.3 70B (70B parameters) requires approximately 76.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.3 70B?

The recommended quantization for Llama 3.3 70B is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.3 70B run at on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, Llama 3.3 70B achieves approximately 14.2 tokens per second decode speed with a time-to-first-token of 13649ms using Q4_K_M quantization.

Can Mac Studio M3 Ultra 256GB run Llama 3.3 70B for coding?

For coding workloads, Llama 3.3 70B on Mac Studio M3 Ultra 256GB receives a A grade with 14.2 tok/s and 128K context.

What context window can Llama 3.3 70B use on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, Llama 3.3 70B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M3 Ultra 256GB as fast as VRAM for Llama 3.3 70B?

Not always. Mac Studio M3 Ultra 256GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for Mac Studio M3 Ultra 256GB See all hardware for Llama 3.3 70B

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