Can Codestral 2 25.08 run on MacBook Pro M3 Pro 36GB?

YES — Runs Great

A83Great

Estimated from fit model

Codestral 2 25.08 needs ~20.6 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q4_K_M quantization, expect ~8 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) — 20.6 GB, 8.2 tok/s, Runs well

20.6 GB required25.9 GB available

80% VRAM used

Fit status

Runs well

Decode

8.2 tok/s

TTFT

23481 ms

Safe context

51K

Memory

20.6 GB / 25.9 GB

Memory breakdown

Weights13.4 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom3.9 GB

See how fast it feels

See how fast it feelsCodestral 2 25.08 on MacBook Pro M3 Pro 36GB
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: 8.2 tok/s decode · 23.5s TTFT (warm) · 21 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	7.7 tok/s	13768 ms	51K
Coding	A	Runs well	7.7 tok/s	25242 ms	51K
Agentic Coding	A	Tight fit	7.7 tok/s	36715 ms	51K
Reasoning	A	Runs well	7.7 tok/s	29831 ms	51K
RAG	A	Tight fit	7.7 tok/s	45894 ms	51K

Quantization options

How Codestral 2 25.08 (22B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	A81
Q3_K_S	3	10.8 GB	Low	A83
NVFP4	4	12.3 GB	Medium	A84
Q4_K_M	4	13.4 GB	Medium	A84
Q5_K_M	5	15.8 GB	High	A84
Q6_KBest for your GPU	6	18.0 GB	High	A84
Q8_0	8	23.5 GB	Very High	F0
F16	16	45.1 GB	Maximum	F0

Get started

Copy-paste commands to run Codestral 2 25.08 on your machine.

Run

lms load codestral-2508 && lms server start

Your hardware

More models your MacBook Pro M3 Pro 36GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	16.6 tok/s
Qwen 3.5 27B	27B	S	7.2 tok/s
Qwen 3.6 27B	27B	S	5.5 tok/s
Qwen 3.6 35B A3B	35B	A	12.1 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	17.1 tok/s

Frequently asked questions

Can MacBook Pro M3 Pro 36GB run Codestral 2 25.08?

Yes, MacBook Pro M3 Pro 36GB can run Codestral 2 25.08 with a A grade (Runs well). Expected decode speed: 7.7 tok/s.

How much VRAM does Codestral 2 25.08 need?

Codestral 2 25.08 (22B parameters) requires approximately 20.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 2 25.08?

The recommended quantization for Codestral 2 25.08 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 2 25.08 run at on MacBook Pro M3 Pro 36GB?

On MacBook Pro M3 Pro 36GB, Codestral 2 25.08 achieves approximately 7.7 tokens per second decode speed with a time-to-first-token of 25242ms using Q4_K_M quantization.

Can MacBook Pro M3 Pro 36GB run Codestral 2 25.08 for coding?

For coding workloads, Codestral 2 25.08 on MacBook Pro M3 Pro 36GB receives a A grade with 7.7 tok/s and 51K context.

What context window can Codestral 2 25.08 use on MacBook Pro M3 Pro 36GB?

On MacBook Pro M3 Pro 36GB, Codestral 2 25.08 can safely use up to 51K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Codestral 2 25.08 feels slow on MacBook Pro M3 Pro 36GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Is unified memory on MacBook Pro M3 Pro 36GB as fast as VRAM for Codestral 2 25.08?

Not always. MacBook Pro M3 Pro 36GB 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 M3 Pro 36GB See all hardware for Codestral 2 25.08

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