Can Codestral 2 25.08 run on MacBook Pro M1 Max 32GB?

YES — Tight Fit

A83Great

Estimated from fit model

Codestral 2 25.08 needs ~20.2 GB VRAM. MacBook Pro M1 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~17 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: 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) — 20.2 GB, 16.6 tok/s, Tight fit

20.2 GB required23.0 GB available

88% VRAM used

Fit status

Tight fit

Decode

16.6 tok/s

TTFT

11687 ms

Safe context

34K

Memory

20.2 GB / 23.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsCodestral 2 25.08 on MacBook Pro M1 Max 32GB
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: 16.6 tok/s decode · 11.7s TTFT (warm) · 41 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	Tight fit	16.6 tok/s	6375 ms	34K
Coding	A	Tight fit	16.6 tok/s	11687 ms	34K
Agentic Coding	A	Runs with offload	16.6 tok/s	16999 ms	34K
Reasoning	A	Tight fit	16.6 tok/s	13812 ms	34K
RAG	A	Runs with offload	16.6 tok/s	21249 ms	34K

Inference speed

Codestral 2 25.08 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Codestral 2 25.08 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~96 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	96.2	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	52.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	41.9	Fits
RTX 4090 24GB	24 GB	Q4_K_M	41.7	Fits
RTX 3090 24GB	24 GB	Q4_K_M	38.2	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	35.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	35.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	34.9	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	33.1	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	22.2	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	18.6	Heavy offload
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	18.1	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	16.6	Fits
RTX 4070 12GB	12 GB	Q4_K_M	6.6	Too big
RTX 3060 12GB	12 GB	Q4_K_M	4.4	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Codestral 2 25.08 (22B params) fits at each quantization level on MacBook Pro M1 Max 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	A83
Q3_K_S	3	10.8 GB	Low	A84
NVFP4	4	12.3 GB	Medium	A85
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 M1 Max 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	A	29.9 tok/s
Qwen 3.5 27B	27B	S	13.3 tok/s
Qwen 3.6 27B	27B	S	11 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	31.5 tok/s
Qwen 3.5 35B A3B	35B	A	26 tok/s

Frequently asked questions

Can MacBook Pro M1 Max 32GB run Codestral 2 25.08?

Yes, MacBook Pro M1 Max 32GB can run Codestral 2 25.08 with a A grade (Tight fit). Expected decode speed: 16.6 tok/s.

How much VRAM does Codestral 2 25.08 need?

Codestral 2 25.08 (22B parameters) requires approximately 20.2 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 M1 Max 32GB?

On MacBook Pro M1 Max 32GB, Codestral 2 25.08 achieves approximately 16.6 tokens per second decode speed with a time-to-first-token of 11687ms using Q4_K_M quantization.

Can MacBook Pro M1 Max 32GB run Codestral 2 25.08 for coding?

For coding workloads, Codestral 2 25.08 on MacBook Pro M1 Max 32GB receives a A grade with 16.6 tok/s and 34K context.

What context window can Codestral 2 25.08 use on MacBook Pro M1 Max 32GB?

On MacBook Pro M1 Max 32GB, Codestral 2 25.08 can safely use up to 34K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M1 Max 32GB as fast as VRAM for Codestral 2 25.08?

Not always. MacBook Pro M1 Max 32GB 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 Max 32GB See all hardware for Codestral 2 25.08

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