Can Codestral Mamba 7B run on MacBook Pro M2 Pro 16GB?

YES — Runs Great

A78Great

Estimated from fit model

Codestral Mamba 7B needs ~7.4 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~38 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) — 7.4 GB, 37.7 tok/s, Runs well

7.4 GB required11.5 GB available

64% VRAM used

Fit status

Runs well

Decode

37.7 tok/s

TTFT

5135 ms

Safe context

151K

Memory

7.4 GB / 11.5 GB

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsCodestral Mamba 7B on MacBook Pro M2 Pro 16GB
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: 37.7 tok/s decode · 5.1s TTFT (warm) · 94 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	37.7 tok/s	2801 ms	151K
Coding	A	Runs well	37.7 tok/s	5135 ms	151K
Agentic Coding	A	Runs well	37.7 tok/s	7469 ms	151K
Reasoning	A	Runs well	37.7 tok/s	6068 ms	151K
RAG	A	Runs well	37.7 tok/s	9336 ms	151K

Quantization options

How Codestral Mamba 7B (7B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A74
Q3_K_S	3	3.4 GB	Low	A75
NVFP4	4	3.9 GB	Medium	A76
Q4_K_M	4	4.3 GB	Medium	A77
Q5_K_M	5	5.0 GB	High	A78
Q6_K	6	5.7 GB	High	A78
Q8_0Best for your GPU	8	7.5 GB	Very High	A77
F16	16	14.3 GB	Maximum	F0

Get started

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

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "mistralai/Mamba-Codestral-7B-v0.1" \
  --hf-file "Mamba-Codestral-7B-v0.1-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your MacBook Pro M2 Pro 16GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	27.4 tok/s
Qwen 3 14B	14B	A	13.8 tok/s
Qwen 3 8B	8B	S	30.8 tok/s
Nemotron Nano 8B	8B	S	30.8 tok/s
Ministral 3 14B	14B	B	13.7 tok/s

Frequently asked questions

Can MacBook Pro M2 Pro 16GB run Codestral Mamba 7B?

Yes, MacBook Pro M2 Pro 16GB can run Codestral Mamba 7B with a A grade (Runs well). Expected decode speed: 37.7 tok/s.

How much VRAM does Codestral Mamba 7B need?

Codestral Mamba 7B (7B parameters) requires approximately 7.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral Mamba 7B?

The recommended quantization for Codestral Mamba 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral Mamba 7B run at on MacBook Pro M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, Codestral Mamba 7B achieves approximately 37.7 tokens per second decode speed with a time-to-first-token of 5135ms using Q4_K_M quantization.

Can MacBook Pro M2 Pro 16GB run Codestral Mamba 7B for coding?

For coding workloads, Codestral Mamba 7B on MacBook Pro M2 Pro 16GB receives a A grade with 37.7 tok/s and 151K context.

What context window can Codestral Mamba 7B use on MacBook Pro M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, Codestral Mamba 7B can safely use up to 151K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M2 Pro 16GB as fast as VRAM for Codestral Mamba 7B?

Not always. MacBook Pro M2 Pro 16GB 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 M2 Pro 16GB See all hardware for Codestral Mamba 7B

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<iframe src="https://willitrunai.com/embed/codestral-mamba-7b-on-m2-pro-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

37.7 tok/s

2801 ms

151K

Coding

Runs well

37.7 tok/s

5135 ms

151K

Agentic Coding

Runs well

37.7 tok/s

7469 ms

151K

Reasoning

Runs well

37.7 tok/s

6068 ms

151K

RAG

Runs well

37.7 tok/s

9336 ms

151K

Quantization options

How Codestral Mamba 7B (7B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A74
Q3_K_S	3	3.4 GB	Low	A75
NVFP4	4	3.9 GB	Medium	A76
Q4_K_M	4	4.3 GB	Medium	A77
Q5_K_M	5	5.0 GB	High	A78
Q6_K	6	5.7 GB	High	A78
Q8_0Best for your GPU	8	7.5 GB	Very High	A77
F16	16	14.3 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen 3.5 9B

27.4 tok/s

Qwen 3 14B

14B

13.8 tok/s

Qwen 3 8B

30.8 tok/s

Nemotron Nano 8B

30.8 tok/s

Ministral 3 14B

14B

13.7 tok/s