Can Devstral 2 123B Instruct run on MacBook Pro M4 Max 128GB?

YES — With Offload

S89Excellent

○Estimated from fit model

Devstral 2 123B Instruct needs ~95.1 GB VRAM. MacBook Pro M4 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~8 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: 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) — 95.1 GB, 8.2 tok/s, Runs with offload (needs ~2.3 GB host RAM)

95.1 GB required92.2 GB available

103% VRAM needed

2.9 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~2.3 GB host RAM)

Decode

8.2 tok/s

TTFT

23719 ms

Safe context

Memory

95.1 GB / 92.2 GB

Memory breakdown

Weights75.0 GB

KV Cache5.4 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsDevstral 2 123B Instruct on MacBook Pro M4 Max 128GB
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: 8.2 tok/s decode · 23.7s TTFT (warm) · 20 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Buy headroom, not only minimum fit

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs with offload (needs ~0.2 GB host RAM)	8.6 tok/s	12249 ms	7K
Coding	S	Runs with offload (needs ~2.3 GB host RAM)	8.2 tok/s	23719 ms	7K
Agentic Coding	A	Very compromised (needs ~6.2 GB host RAM)	7.5 tok/s	37462 ms	7K
Reasoning	S	Runs with offload (needs ~2.3 GB host RAM)	8.2 tok/s	28032 ms	7K
RAG	A	Very compromised (needs ~6.2 GB host RAM)	7.5 tok/s	46827 ms	7K

Quantization options

How Devstral 2 123B Instruct (123B params) fits at each quantization level on MacBook Pro M4 Max 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	48.0 GB	Low	S91
Q3_K_S	3	60.3 GB	Low	S91
NVFP4Best for your GPU	4	68.9 GB	Medium	S91
Q4_K_M	4	75.0 GB	Medium	F0
Q5_K_M	5	88.6 GB	High	F0
Q6_K	6	100.9 GB	High	F0
Q8_0	8	131.6 GB	Very High	F0
F16	16	252.2 GB	Maximum	F0

Get started

Copy-paste commands to run Devstral 2 123B Instruct on your machine.

Run

lms load Devstral-2-123B-Instruct-2512 && lms server start

Frequently asked questions

See all results for MacBook Pro M4 Max 128GB See all hardware for Devstral 2 123B Instruct

URL: https://willitrunai.com/can-run/devstral-2-123b-on-m4-max-128gb

⇱ Devstral 2 123B Instruct on MacBook Pro M4 Max 128GB? YES