Can Mistral Small 4 119B run on MacBook Pro M2 Max 96GB?

YES — With Q3_K_S

A79Great

○Estimated from fit model

Mistral Small 4 119B needs ~74.9 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q3_K_S quantization, expect ~16 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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.

Mistral Small 4 119B at Q4_K_M needs 89.2 GB — too much for MacBook Pro M2 Max 96GB (69.1 GB). Runs at Q3_K_S (74.9 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 89.2 GB, exceeds 69.1 GB available

89.2 GB required69.1 GB available

129% VRAM needed

20.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

10.9 tok/s

TTFT

17736 ms

Safe context

Memory

89.2 GB / 69.1 GB

Offload

20%

Memory breakdown

Weights72.6 GB

KV Cache5.4 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsMistral Small 4 119B on MacBook Pro M2 Max 96GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
 if len(arr) <= 1:
 return arr
 pivot = arr[len(arr) // 2]
 left = [x for x in arr if x < pivot]
 middle = [x for x in arr if x == pivot]
 right = [x for x in arr if x > pivot]
 return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
 """Sort a list using the quicksort algorithm.

 Args:
 arr: List of integers to sort.

 Returns:
 A new sorted list.
 """
 if len(arr) <= 1:
 return arr
 pivot = arr[len(arr) // 2]
 left = [x for x in arr if x < pivot]
 middle = [x for x in arr if x == pivot]
 right = [x for x in arr if x > pivot]
 return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 10.9 tok/s decode · 17.7s TTFT (warm) · 27 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 4.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	10.4 tok/s	10139 ms	4K
Coding	F	Too heavy	10.0 tok/s	19288 ms	4K
Agentic Coding	F	Too heavy	9.4 tok/s	30064 ms	4K
Reasoning	F	Too heavy	10.0 tok/s	22795 ms	4K
RAG	F	Too heavy	9.4 tok/s	37580 ms	4K

Quantization options

How Mistral Small 4 119B (119B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	46.4 GB	Low	S88
Q3_K_S	3	58.3 GB	Low	F0

Get started

Copy-paste commands to run Mistral Small 4 119B on your machine.

Run

lms load Mistral-Small-4-119B-2603 && lms server start

Upgrade options

Hardware that runs Mistral Small 4 119B well

MacBook Pro M3 Max 128GBBudget pick

128 GB Unified (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.16 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$2,499 MSRP

Mac Studio M2 Ultra 128GBBest value

128 GB Unified (+32)800 GB/s (+400)

Makes the model fit on the accelerator instead of staying completely out of reach.30.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$3,999 MSRP

Mac Studio M1 Ultra 128GBApple upgrade

128 GB Unified (+32)800 GB/s (+400)

Makes the model fit on the accelerator instead of staying completely out of reach.29.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$3,999 MSRP

AMD Instinct MI250X 128GBBiggest leap

128 GB VRAM (+32)3200 GB/s (+2800)

Makes the model fit on the accelerator instead of staying completely out of reach.108.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$15,000 MSRP

Frequently asked questions

See all results for MacBook Pro M2 Max 96GB See all hardware for Mistral Small 4 119B

URL: https://willitrunai.com/can-run/mistral-small-4-119b-on-m2-max-96gb

⇱

Can Mistral Small 4 119B run on MacBook Pro M2 Max 96GB?

YES — With Q3_K_S

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs Mistral Small 4 119B well

Frequently asked questions