Can Mamba Codestral 7B v0.1 run on MacBook Pro M4 Max 64GB?

YES — Runs Great

C47Usable

Estimated from fit model

Mamba Codestral 7B v0.1 needs ~12.9 GB VRAM. MacBook Pro M4 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~93 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) — 12.9 GB, 92.6 tok/s, Runs well

12.9 GB required46.1 GB available

28% VRAM used

Fit status

Runs well

Decode

92.6 tok/s

TTFT

2090 ms

Safe context

663K

Memory

12.9 GB / 46.1 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsMamba Codestral 7B v0.1 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: 92.6 tok/s decode · 2.1s TTFT (warm) · 232 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	C	Runs well	92.6 tok/s	1140 ms	663K
Coding	C	Runs well	92.6 tok/s	2090 ms	663K
Agentic Coding	C	Runs well	92.6 tok/s	3040 ms	663K
Reasoning	C	Runs well	92.6 tok/s	2470 ms	663K
RAG	C	Runs well	92.6 tok/s	3800 ms	663K

Inference speed

Mamba Codestral 7B v0.1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Mamba Codestral 7B v0.1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 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	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	98.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	98.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	98.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	92.6	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	92.6	Fits
RTX 4070 12GB	12 GB	Q4_K_M	88.5	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	64.6	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	59.3	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	56.6	Fits
RTX 3060 12GB	12 GB	Q4_K_M	55.6	Fits
RTX 4060 8GB	8 GB	Q4_K_M	53.5	Tight

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 Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on MacBook Pro M4 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C41
Q3_K_S	3	3.4 GB	Low	C41
NVFP4	4	3.9 GB	Medium	C41
Q4_K_M	4	4.3 GB	Medium	C41
Q5_K_M	5	5.0 GB	High	C41
Q6_K	6	5.7 GB	High	C42
Q8_0	8	7.5 GB	Very High	C42
F16Best for your GPU	16	14.3 GB	Maximum	C44

Get started

Copy-paste commands to run Mamba Codestral 7B v0.1 on your machine.

Run

lms load hf-gabriellarson--mamba-codestral-7b-v0-1-gguf && lms server start

Frequently asked questions

Can MacBook Pro M4 Max 64GB run Mamba Codestral 7B v0.1?

Yes, MacBook Pro M4 Max 64GB can run Mamba Codestral 7B v0.1 with a C grade (Runs well). Expected decode speed: 92.6 tok/s.

How much VRAM does Mamba Codestral 7B v0.1 need?

Mamba Codestral 7B v0.1 (7B parameters) requires approximately 12.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Mamba Codestral 7B v0.1?

The recommended quantization for Mamba Codestral 7B v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Mamba Codestral 7B v0.1 run at on MacBook Pro M4 Max 64GB?

On MacBook Pro M4 Max 64GB, Mamba Codestral 7B v0.1 achieves approximately 92.6 tokens per second decode speed with a time-to-first-token of 2090ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 64GB run Mamba Codestral 7B v0.1 for coding?

For coding workloads, Mamba Codestral 7B v0.1 on MacBook Pro M4 Max 64GB receives a C grade with 92.6 tok/s and 663K context.

What context window can Mamba Codestral 7B v0.1 use on MacBook Pro M4 Max 64GB?

On MacBook Pro M4 Max 64GB, Mamba Codestral 7B v0.1 can safely use up to 663K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Max 64GB as fast as VRAM for Mamba Codestral 7B v0.1?

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.

See all results for MacBook Pro M4 Max 64GB See all hardware for Mamba Codestral 7B v0.1

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