Can Devstral Small 1.1 run on MacBook Pro M3 Max 48GB?

YES — Runs Great

S90Excellent

Estimated from fit model

Devstral Small 1.1 needs ~23.2 GB VRAM. MacBook Pro M3 Max 48GB has 34.6 GB. With Q4_K_M quantization, expect ~18 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) — 23.2 GB, 17.6 tok/s, Runs well

23.2 GB required34.6 GB available

67% VRAM used

Fit status

Runs well

Decode

17.6 tok/s

TTFT

10986 ms

Safe context

91K

Memory

23.2 GB / 34.6 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom5.2 GB

See how fast it feels

See how fast it feelsDevstral Small 1.1 on MacBook Pro M3 Max 48GB
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: 17.6 tok/s decode · 11.0s TTFT (warm) · 44 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	17.6 tok/s	5992 ms	91K
Coding	S	Runs well	17.6 tok/s	10986 ms	91K
Agentic Coding	S	Runs well	17.6 tok/s	15979 ms	91K
Reasoning	S	Runs well	17.6 tok/s	12983 ms	91K
RAG	S	Runs well	17.6 tok/s	19974 ms	91K

Inference speed

Devstral Small 1.1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Devstral Small 1.1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~88 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	88.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	56.3	Tight
RX 7900 XTX 24GB	24 GB	Q4_K_M	50.8	Tight
RTX 3090 24GB	24 GB	Q4_K_M	48.1	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	40.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	36.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	36.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	34.1	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	32.3	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	23.2	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	21.3	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	17.6	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	16.2	Fits
RTX 4070 12GB	12 GB	Q4_K_M	7.5	Too big
RTX 3060 12GB	12 GB	Q4_K_M	4.7	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.2	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Devstral Small 1.1 (24B params) fits at each quantization level on MacBook Pro M3 Max 48GB (34.6 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A85
Q3_K_S	3	11.8 GB	Low	S86
NVFP4	4	13.4 GB	Medium	S86
Q4_K_M	4	14.6 GB	Medium	S87
Q5_K_M	5	17.3 GB	High	S88
Q6_K	6	19.7 GB	High	S89
Q8_0Best for your GPU	8	25.7 GB	Very High	S88
F16	16	49.2 GB	Maximum	F0

Get started

Copy-paste commands to run Devstral Small 1.1 on your machine.

Run

lms load Devstral-Small-2507 && lms server start

Your hardware

More models your MacBook Pro M3 Max 48GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	36.3 tok/s
Qwen 3.5 27B	27B	S	15.7 tok/s
Qwen 3.6 27B	27B	S	12 tok/s
Qwen 3.6 35B A3B	35B	S	33.5 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	37.5 tok/s

Frequently asked questions

Can MacBook Pro M3 Max 48GB run Devstral Small 1.1?

Yes, MacBook Pro M3 Max 48GB can run Devstral Small 1.1 with a S grade (Runs well). Expected decode speed: 17.6 tok/s.

How much VRAM does Devstral Small 1.1 need?

Devstral Small 1.1 (24B parameters) requires approximately 23.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Devstral Small 1.1?

The recommended quantization for Devstral Small 1.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Devstral Small 1.1 run at on MacBook Pro M3 Max 48GB?

On MacBook Pro M3 Max 48GB, Devstral Small 1.1 achieves approximately 17.6 tokens per second decode speed with a time-to-first-token of 10986ms using Q4_K_M quantization.

Can MacBook Pro M3 Max 48GB run Devstral Small 1.1 for coding?

For coding workloads, Devstral Small 1.1 on MacBook Pro M3 Max 48GB receives a S grade with 17.6 tok/s and 91K context.

What context window can Devstral Small 1.1 use on MacBook Pro M3 Max 48GB?

On MacBook Pro M3 Max 48GB, Devstral Small 1.1 can safely use up to 91K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M3 Max 48GB as fast as VRAM for Devstral Small 1.1?

Not always. MacBook Pro M3 Max 48GB 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 MacBook Pro M3 Max 48GB See all hardware for Devstral Small 1.1

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/devstral-small-2507-on-m3-max-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: