Can Mixtral 8x7B run on Mac Studio M3 Ultra 96GB?

YES — Runs Great

B68Good

○Estimated from fit model

Mixtral 8x7B needs ~41.9 GB VRAM. Mac Studio M3 Ultra 96GB has 69.1 GB. With Q4_K_M quantization, expect ~40 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) — 41.9 GB, 40.1 tok/s, Runs well

41.9 GB required69.1 GB available

61% VRAM used

Fit status

Runs well

Decode

40.1 tok/s

TTFT

4833 ms

Safe context

33K

Memory

41.9 GB / 69.1 GB

Memory breakdown

Weights28.7 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsMixtral 8x7B 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: 40.1 tok/s decode · 4.8s TTFT (warm) · 100 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	B	Runs well	40.1 tok/s	2636 ms	33K
Coding	B	Runs well	40.1 tok/s	4833 ms	33K
Agentic Coding	B	Runs well	40.1 tok/s	7030 ms	33K
Reasoning	B	Runs well	40.1 tok/s	5712 ms	33K
RAG	B	Runs well	40.1 tok/s	8787 ms	33K

Quantization options

How Mixtral 8x7B (47B params) fits at each quantization level on Mac Studio M3 Ultra 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	18.3 GB	Low	B58
Q3_K_S	3	23.0 GB	Low	B59
NVFP4	4

Get started

Copy-paste commands to run Mixtral 8x7B on your machine.

Run

ollama run mixtral

Upgrade options

Hardware that runs Mixtral 8x7B well

👁 NVIDIA
NVIDIA A100 80GBBudget pick

2039 GB/s (+1220)

Raises estimated decode speed by about 207%.123.2 tok/s decode

Raises estimated decode speed by about 207%.

~$15,000 MSRP

👁 NVIDIA
NVIDIA A800 80GBBest value

1935 GB/s (+1116)

Raises estimated decode speed by about 171%.108.6 tok/s decode

Raises estimated decode speed by about 171%.