Is unified memory on Mac Studio M2 Ultra 64GB as fast as VRAM for Mistral Small 3.1 24B?

Not always. Mac Studio M2 Ultra 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 Mistral Small 3.1 24B run on Mac Studio M2 Ultra 64GB?

YES — Runs Great

A81Great

Estimated from fit model

Mistral Small 3.1 24B needs ~24.9 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~32 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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, 34.1 tok/s, Runs well

24.9 GB required46.1 GB available

54% VRAM used

Fit status

Runs well

Decode

34.1 tok/s

TTFT

5682 ms

Safe context

131K

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 feelsMistral Small 3.1 24B on Mac Studio M2 Ultra 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: 34.1 tok/s decode · 5.7s TTFT (warm) · 85 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	34.1 tok/s	3099 ms	131K
Coding	A	Runs well	31.7 tok/s	6108 ms	131K
Agentic Coding	A	Runs well	34.1 tok/s	8265 ms	131K
Reasoning	A	Runs well	34.1 tok/s	6715 ms	131K
RAG	A	Runs well	34.1 tok/s	10331 ms	131K

Quantization options

How Mistral Small 3.1 24B (24B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A74
Q3_K_S	3	11.8 GB	Low	A75
NVFP4	4

Get started

Copy-paste commands to run Mistral Small 3.1 24B on your machine.

Run

ollama run mistral-small:24b

Your hardware

More models your Mac Studio M2 Ultra 64GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	70.2 tok/s
Qwen 3.5 27B	27B	S	30.4 tok/s

Frequently asked questions

Can Mac Studio M2 Ultra 64GB run Mistral Small 3.1 24B?

Yes, Mac Studio M2 Ultra 64GB can run Mistral Small 3.1 24B with a A grade (Runs well). Expected decode speed: 31.7 tok/s.

How much VRAM does Mistral Small 3.1 24B need?

Mistral Small 3.1 24B (24B parameters) requires approximately 24.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Small 3.1 24B?

The recommended quantization for Mistral Small 3.1 24B is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Small 3.1 24B run at on Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, Mistral Small 3.1 24B achieves approximately 31.7 tokens per second decode speed with a time-to-first-token of 6108ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 64GB run Mistral Small 3.1 24B for coding?

For coding workloads, Mistral Small 3.1 24B on Mac Studio M2 Ultra 64GB receives a A grade with 31.7 tok/s and 131K context.

What context window can Mistral Small 3.1 24B use on Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, Mistral Small 3.1 24B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Mac Studio M2 Ultra 64GB See all hardware for Mistral Small 3.1 24B

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