Can Mixtral 8x7B run on MacBook Pro M2 Max 96GB?

YES — Runs Great

B65Good

○Estimated from fit model

Mixtral 8x7B needs ~41.9 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~17 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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, 16.7 tok/s, Runs well

41.9 GB required69.1 GB available

61% VRAM used

Fit status

Runs well

Decode

16.7 tok/s

TTFT

11601 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 MacBook Pro M2 Max 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: 16.7 tok/s decode · 11.6s TTFT (warm) · 42 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	16.7 tok/s	6328 ms	33K
Coding	B	Runs well	16.7 tok/s	11601 ms	33K
Agentic Coding	B	Runs well	16.7 tok/s	16875 ms	33K
Reasoning	B	Runs well	16.7 tok/s	13711 ms	33K
RAG	B	Runs well	16.7 tok/s	21093 ms	33K

Quantization options

How Mixtral 8x7B (47B params) fits at each quantization level on MacBook Pro M2 Max 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	26.3 GB	Medium	B60
Q4_K_M	4	28.7 GB	Medium	B60
Q5_K_M	5	33.8 GB	High	B62
Q6_K	6	38.5 GB	High	B63
Q8_0Best for your GPU	8	50.3 GB	Very High	B63
F16	16	96.4 GB	Maximum	F0

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
RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick

1792 GB/s (+1392)

Raises estimated decode speed by about 549%.108.3 tok/s decode

Raises estimated decode speed by about 549%.

Adds memory headroom for longer context windows and future model growth.

~$9,999 MSRP

👁 NVIDIA
RTX PRO 6000 Blackwell Server Edition 96GBBest value

1597 GB/s (+1197)

Raises estimated decode speed by about 478%.96.5 tok/s decode

Raises estimated decode speed by about 478%.