Can Devstral 2 123B Instruct run on Mac Studio M3 Ultra 256GB?

YES — Runs Great

S90Excellent

Estimated from fit model

Devstral 2 123B Instruct needs ~108.9 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~8 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) — 108.9 GB, 8.1 tok/s, Runs well

108.9 GB required184.3 GB available

59% VRAM used

Fit status

Runs well

Decode

8.1 tok/s

TTFT

23984 ms

Safe context

241K

Memory

108.9 GB / 184.3 GB

Memory breakdown

Weights75.0 GB

KV Cache5.4 GB

Runtime0.9 GB

Headroom27.6 GB

See how fast it feels

See how fast it feelsDevstral 2 123B Instruct 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: 8.1 tok/s decode · 24.0s TTFT (warm) · 20 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	8.1 tok/s	13082 ms	241K
Coding	S	Runs well	8.1 tok/s	23984 ms	241K
Agentic Coding	S	Runs well	8.1 tok/s	34886 ms	241K
Reasoning	S	Runs well	8.1 tok/s	28345 ms	241K
RAG	S	Runs well	8.1 tok/s	43607 ms	241K

Quantization options

How Devstral 2 123B Instruct (123B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	48.0 GB	Low	A85
Q3_K_S	3	60.3 GB	Low	S86
NVFP4	4	68.9 GB	Medium	S87
Q4_K_M	4	75.0 GB	Medium	S88
Q5_K_M	5	88.6 GB	High	S89
Q6_K	6	100.9 GB	High	S91
Q8_0Best for your GPU	8	131.6 GB	Very High	S91
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

Can Mac Studio M3 Ultra 256GB run Devstral 2 123B Instruct?

Yes, Mac Studio M3 Ultra 256GB can run Devstral 2 123B Instruct with a S grade (Runs well). Expected decode speed: 8.1 tok/s.

How much VRAM does Devstral 2 123B Instruct need?

Devstral 2 123B Instruct (123B parameters) requires approximately 108.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Devstral 2 123B Instruct?

The recommended quantization for Devstral 2 123B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Devstral 2 123B Instruct run at on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, Devstral 2 123B Instruct achieves approximately 8.1 tokens per second decode speed with a time-to-first-token of 23984ms using Q4_K_M quantization.

Can Mac Studio M3 Ultra 256GB run Devstral 2 123B Instruct for coding?

For coding workloads, Devstral 2 123B Instruct on Mac Studio M3 Ultra 256GB receives a S grade with 8.1 tok/s and 241K context.

What context window can Devstral 2 123B Instruct use on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, Devstral 2 123B Instruct can safely use up to 241K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M3 Ultra 256GB as fast as VRAM for Devstral 2 123B Instruct?

Not always. Mac Studio M3 Ultra 256GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for Mac Studio M3 Ultra 256GB See all hardware for Devstral 2 123B Instruct

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