Can Devstral Small 1.1 run on MacBook Pro M1 Pro 32GB?

YES — Tight Fit

S86Excellent

Estimated from fit model

Devstral Small 1.1 needs ~21.4 GB VRAM. MacBook Pro M1 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~9 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 21.4 GB, 9.5 tok/s, Tight fit

21.4 GB required23.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

9.5 tok/s

TTFT

20281 ms

Safe context

27K

Memory

21.4 GB / 23.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsDevstral Small 1.1 on MacBook Pro M1 Pro 32GB
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: 9.5 tok/s decode · 20.3s TTFT (warm) · 24 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	Tight fit	8.9 tok/s	11892 ms	27K
Coding	S	Tight fit	8.9 tok/s	21802 ms	27K
Agentic Coding	S	Runs with offload	8.3 tok/s	34049 ms	27K
Reasoning	S	Tight fit	8.9 tok/s	25766 ms	27K
RAG	S	Runs with offload	8.3 tok/s	42562 ms	27K

Quantization options

How Devstral Small 1.1 (24B params) fits at each quantization level on MacBook Pro M1 Pro 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	S89
Q3_K_S	3	11.8 GB	Low	S90
NVFP4	4	13.4 GB	Medium	S90
Q4_K_M	4	14.6 GB	Medium	S89
Q5_K_MBest for your GPU	5	17.3 GB	High	S89
Q6_K	6	19.7 GB	High	F0
Q8_0	8	25.7 GB	Very High	F0
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 M1 Pro 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	A	17.7 tok/s
Qwen 3.5 27B	27B	S	7.9 tok/s
Qwen 3.6 27B	27B	S	6.5 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	18.6 tok/s
Qwen 3.5 35B A3B	35B	A	15.4 tok/s

Frequently asked questions

Can MacBook Pro M1 Pro 32GB run Devstral Small 1.1?

Yes, MacBook Pro M1 Pro 32GB can run Devstral Small 1.1 with a S grade (Tight fit). Expected decode speed: 8.9 tok/s.

How much VRAM does Devstral Small 1.1 need?

Devstral Small 1.1 (24B parameters) requires approximately 21.4 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 M1 Pro 32GB?

On MacBook Pro M1 Pro 32GB, Devstral Small 1.1 achieves approximately 8.9 tokens per second decode speed with a time-to-first-token of 21802ms using Q4_K_M quantization.

Can MacBook Pro M1 Pro 32GB run Devstral Small 1.1 for coding?

For coding workloads, Devstral Small 1.1 on MacBook Pro M1 Pro 32GB receives a S grade with 8.9 tok/s and 27K context.

What context window can Devstral Small 1.1 use on MacBook Pro M1 Pro 32GB?

On MacBook Pro M1 Pro 32GB, Devstral Small 1.1 can safely use up to 27K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Devstral Small 1.1 feels slow on MacBook Pro M1 Pro 32GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Is unified memory on MacBook Pro M1 Pro 32GB as fast as VRAM for Devstral Small 1.1?

Not always. MacBook Pro M1 Pro 32GB 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 M1 Pro 32GB See all hardware for Devstral Small 1.1

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