Is unified memory on MacBook Pro M4 Max 64GB as fast as VRAM for Devstral Small 2 24B Instruct?

Not always. MacBook Pro M4 Max 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.

Can Devstral Small 2 24B Instruct run on MacBook Pro M4 Max 64GB?

YES — Runs Great

S92Excellent

Measured on real hardware· m4-max-64gb

Devstral Small 2 24B Instruct needs ~24.9 GB VRAM. MacBook Pro M4 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~36 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 24.9 GB, 36.8 tok/s, Runs well

24.9 GB required46.1 GB available

54% VRAM used

Fit status

Runs well

Decode

36.8 tok/s

TTFT

5268 ms

Safe context

155K

Memory

24.9 GB / 46.1 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsDevstral Small 2 24B Instruct on MacBook Pro M4 Max 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: 36.8 tok/s decode · 5.3s TTFT (warm) · 92 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	36.8 tok/s	2873 ms	155K
Coding	S	Runs well	36.0 tok/s	8240 ms	155K
Agentic Coding	S	Runs well	36.8 tok/s	7662 ms	155K
Reasoning	S	Runs well	36.8 tok/s	6226 ms	155K
RAG	S	Runs well	36.8 tok/s	9578 ms	155K

Inference speed

Devstral Small 2 24B Instruct inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Devstral Small 2 24B Instruct 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

Quantization options

How Devstral Small 2 24B Instruct (24B params) fits at each quantization level on MacBook Pro M4 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A84
Q3_K_S	3	11.8 GB	Low	S85
NVFP4	4

Get started

Copy-paste commands to run Devstral Small 2 24B Instruct on your machine.

Run

ollama run devstral-small-2

Your hardware

More models your MacBook Pro M4 Max 64GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	52 tok/s
Qwen 3.5 27B	27B	S	36.1 tok/s

Frequently asked questions

Can MacBook Pro M4 Max 64GB run Devstral Small 2 24B Instruct?

Yes, MacBook Pro M4 Max 64GB can run Devstral Small 2 24B Instruct with a S grade (Runs well). Expected decode speed: 36.0 tok/s.

How much VRAM does Devstral Small 2 24B Instruct need?

Devstral Small 2 24B Instruct (24B parameters) requires approximately 24.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Devstral Small 2 24B Instruct?

The recommended quantization for Devstral Small 2 24B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Devstral Small 2 24B Instruct run at on MacBook Pro M4 Max 64GB?

On MacBook Pro M4 Max 64GB, Devstral Small 2 24B Instruct achieves approximately 36.0 tokens per second decode speed with a time-to-first-token of 8240ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 64GB run Devstral Small 2 24B Instruct for coding?

For coding workloads, Devstral Small 2 24B Instruct on MacBook Pro M4 Max 64GB receives a S grade with 36.0 tok/s and 155K context.

What context window can Devstral Small 2 24B Instruct use on MacBook Pro M4 Max 64GB?

On MacBook Pro M4 Max 64GB, Devstral Small 2 24B Instruct can safely use up to 155K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for MacBook Pro M4 Max 64GB See all hardware for Devstral Small 2 24B Instruct

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