Can DevStral 7B run on Mac mini M4 64GB?

YES — Runs Great

B69Good

Estimated — low-sample bucket· few comparable runs

DevStral 7B needs ~14.0 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~20 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) — 14.0 GB, 20.0 tok/s, Runs well

14.0 GB required46.1 GB available

30% VRAM used

Fit status

Runs well

Decode

20.0 tok/s

TTFT

9674 ms

Safe context

Memory

14.0 GB / 46.1 GB

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsDevStral 7B on Mac mini M4 64GB
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: 20.0 tok/s decode · 9.7s TTFT (warm) · 50 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	B	Runs well	20.0 tok/s	5277 ms	8K
Coding	B	Runs well	20.2 tok/s	9568 ms	8K
Agentic Coding	B	Runs well	20.0 tok/s	14072 ms	8K
Reasoning	B	Runs well	20.0 tok/s	11433 ms	8K
RAG	B	Runs well	20.0 tok/s	17590 ms	8K

Quantization options

How DevStral 7B (7B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B67
Q3_K_S	3	3.4 GB	Low	B67
NVFP4	4

Get started

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

Run

ollama run devstral

Upgrade options

Hardware that runs DevStral 7B well

MacBook Pro M4 Max 96GBBudget pick

96 GB Unified (+32)546 GB/s (+426)

Raises estimated decode speed by about 372%.94.4 tok/s decode

Raises estimated decode speed by about 372%.

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

MacBook Pro M3 Max 128GBBest value

128 GB Unified (+64)400 GB/s (+280)

Raises estimated decode speed by about 202%.60.4 tok/s decode

Raises estimated decode speed by about 202%.

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

Mac Studio M3 Ultra 96GBApple upgrade

96 GB Unified (+32)819 GB/s (+699)

Raises estimated decode speed by about 390%.98 tok/s decode

Raises estimated decode speed by about 390%.

Adds memory headroom for longer context windows and future model growth.

~$3,999 MSRP

Frequently asked questions

Can Mac mini M4 64GB run DevStral 7B?

Yes, Mac mini M4 64GB can run DevStral 7B with a B grade (Runs well). Expected decode speed: 20.2 tok/s.

How much VRAM does DevStral 7B need?

DevStral 7B (7B parameters) requires approximately 14.0 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 Mac mini M4 64GB?

On Mac mini M4 64GB, DevStral 7B achieves approximately 20.2 tokens per second decode speed with a time-to-first-token of 9568ms using Q4_K_M quantization.

Can Mac mini M4 64GB run DevStral 7B for coding?

For coding workloads, DevStral 7B on Mac mini M4 64GB receives a B grade with 20.2 tok/s and 8K context.

What context window can DevStral 7B use on Mac mini M4 64GB?

On Mac mini M4 64GB, 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 Mac mini M4 64GB as fast as VRAM for DevStral 7B?

Not always. Mac mini M4 64GB 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 mini M4 64GB See all hardware for DevStral 7B

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