Can DevStral 7B run on MacBook Pro M1 Pro 16GB?

YES — Runs Great

A79Great

Estimated from fit model

DevStral 7B needs ~8.9 GB VRAM. MacBook Pro M1 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~33 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 8.9 GB, 32.7 tok/s, Runs well

8.9 GB required11.5 GB available

77% VRAM used

Fit status

Runs well

Decode

32.7 tok/s

TTFT

5915 ms

Safe context

Memory

8.9 GB / 11.5 GB

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsDevStral 7B on MacBook Pro M1 Pro 16GB
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: 32.7 tok/s decode · 5.9s TTFT (warm) · 82 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	A	Runs well	32.7 tok/s	3227 ms	8K
Coding	A	Runs well	32.7 tok/s	5915 ms	8K
Agentic Coding	A	Tight fit	32.7 tok/s	8604 ms	8K
Reasoning	A	Runs well	32.7 tok/s	6991 ms	8K
RAG	A	Tight fit	32.7 tok/s	10755 ms	8K

Quantization options

How DevStral 7B (7B params) fits at each quantization level on MacBook Pro M1 Pro 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A74
Q3_K_S	3	3.4 GB	Low	A75
NVFP4	4	3.9 GB	Medium	A76
Q4_K_M	4	4.3 GB	Medium	A77
Q5_K_M	5	5.0 GB	High	A78
Q6_K	6	5.7 GB	High	A78
Q8_0Best for your GPU	8	7.5 GB	Very High	A77
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run DevStral 7B on your machine.

Run

ollama run devstral

Your hardware

More models your MacBook Pro M1 Pro 16GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	25.5 tok/s
Qwen 3 14B	14B	A	12.8 tok/s
Qwen 3 8B	8B	S	28.6 tok/s
Nemotron Nano 8B	8B	S	28.6 tok/s
Ministral 3 14B	14B	B	12.7 tok/s

Frequently asked questions

Can MacBook Pro M1 Pro 16GB run DevStral 7B?

Yes, MacBook Pro M1 Pro 16GB can run DevStral 7B with a A grade (Runs well). Expected decode speed: 32.7 tok/s.

How much VRAM does DevStral 7B need?

DevStral 7B (7B parameters) requires approximately 8.9 GB of memory with Q4_K_M quantization.

What is the best quantization for DevStral 7B?

The recommended quantization for DevStral 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will DevStral 7B run at on MacBook Pro M1 Pro 16GB?

On MacBook Pro M1 Pro 16GB, DevStral 7B achieves approximately 32.7 tokens per second decode speed with a time-to-first-token of 5915ms using Q4_K_M quantization.

Can MacBook Pro M1 Pro 16GB run DevStral 7B for coding?

For coding workloads, DevStral 7B on MacBook Pro M1 Pro 16GB receives a A grade with 32.7 tok/s and 8K context.

What context window can DevStral 7B use on MacBook Pro M1 Pro 16GB?

On MacBook Pro M1 Pro 16GB, DevStral 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M1 Pro 16GB as fast as VRAM for DevStral 7B?

Not always. MacBook Pro M1 Pro 16GB 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 16GB See all hardware for DevStral 7B

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