Can Mistral Small 4 119B run on Mac Studio M3 Ultra 256GB?

YES — Runs Great

S92Excellent

○Estimated from fit model

Mistral Small 4 119B needs ~106.5 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~38 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) — 106.5 GB, 37.6 tok/s, Runs well

106.5 GB required184.3 GB available

58% VRAM used

Fit status

Runs well

Decode

37.6 tok/s

TTFT

5145 ms

Safe context

248K

Memory

106.5 GB / 184.3 GB

Memory breakdown

Weights72.6 GB

KV Cache5.4 GB

Runtime0.9 GB

Headroom27.6 GB

See how fast it feels

See how fast it feelsMistral Small 4 119B on Mac Studio M3 Ultra 256GB
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: 37.6 tok/s decode · 5.1s TTFT (warm) · 94 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	S	Runs well	37.6 tok/s	2806 ms	248K
Coding	S	Runs well	37.6 tok/s	5145 ms	248K
Agentic Coding	S	Runs well	37.6 tok/s	7484 ms	248K
Reasoning	S	Runs well	37.6 tok/s	6081 ms	248K
RAG	S	Runs well	37.6 tok/s	9355 ms	248K

Quantization options

How Mistral Small 4 119B (119B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	46.4 GB	Low	A82
Q3_K_S	3	58.3 GB	Low	A83
NVFP4	4	66.6 GB	Medium	A84
Q4_K_M	4	72.6 GB	Medium	A85
Q5_K_M	5	85.7 GB	High	S86
Q6_K	6	97.6 GB	High	S88
Q8_0Best for your GPU	8	127.3 GB	Very High	S88
F16	16	244.0 GB	Maximum	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

Your hardware

More models your Mac Studio M3 Ultra 256GB can run

Model	Params	Grade	Decode
👁 Mistral Devstral 2 123B Instruct	123B	S	8.1 tok/s
👁 Alibaba Qwen 3.5 122B A10B	122B	S	34.7 tok/s
👁 DeepSeek DeepSeek V4 Flash	284B	S	17.8 tok/s

Frequently asked questions

See all results for Mac Studio M3 Ultra 256GB See all hardware for Mistral Small 4 119B

URL: https://willitrunai.com/can-run/mistral-small-4-119b-on-m3-ultra-256gb

⇱ Mistral Small 4 119B on Mac Studio M3 Ultra 256GB? YES