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⇱ Llama 3.3 70B on Mac Studio M3 Ultra 96GB? TIGHT FIT

Can Llama 3.3 70B run on Mac Studio M3 Ultra 96GB?

YES — Tight Fit

A82Great
○Estimated from fit model

Llama 3.3 70B needs ~58.9 GB VRAM. Mac Studio M3 Ultra 96GB has 69.1 GB. With Q4_K_M quantization, expect ~14 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: HighStack: StandardBottleneck: Balanced
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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) — 58.9 GB, 14.2 tok/s, Tight fit
58.9 GB required69.1 GB available
85% VRAM used

Fit status

Tight fit

Decode

14.2 tok/s

TTFT

13649 ms

Safe context

50K

Memory

58.9 GB / 69.1 GB

Memory breakdown

Weights42.7 GB
KV Cache4.9 GB
Runtime0.9 GB
Headroom10.4 GB

See how fast it feels

See how fast it feelsLlama 3.3 70B on Mac Studio M3 Ultra 96GB
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

WorkloadGradeFitDecodeTTFTContext
ChatSRuns well14.2 tok/s7445 ms50K
CodingATight fit14.2 tok/s13649 ms50K
Agentic CodingATight fit14.2 tok/s19854 ms50K
ReasoningATight fit14.2 tok/s16131 ms50K
RAGATight fit14.2 tok/s24817 ms50K

Quantization options

How Llama 3.3 70B (70B params) fits at each quantization level on Mac Studio M3 Ultra 96GB (69.1 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
27.3 GB
LowA79
Q3_K_S
3
34.3 GB
LowA82
NVFP4
4
39.2 GB
MediumA82
Q4_K_M
4
42.7 GB
MediumA82
Q5_K_MBest for your GPU
5
50.4 GB
HighA82
Q6_K
6
57.4 GB
HighF0
Q8_0
8
74.9 GB
Very HighF0
F16
16
143.5 GB
MaximumF0

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 96GB can run

ModelParamsGradeDecodeCapabilities
👁 Cohere
Command A 111B		111BA6.8 tok/s
👁 Alibaba
Qwen 2.5 VL 72B		72BS13.8 tok/s
👁 Alibaba
Qwen3-Coder-Next		80BS37.6 tok/s

Frequently asked questions

See all results for Mac Studio M3 Ultra 96GBSee all hardware for Llama 3.3 70B