Can Mamba Codestral 7B v0.1 run on MacBook Air M2 16GB?

YES — Runs Great

C51Usable

Estimated from fit model

Mamba Codestral 7B v0.1 needs ~7.7 GB VRAM. MacBook Air M2 16GB has 11.5 GB. With Q4_K_M quantization, expect ~18 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) — 7.7 GB, 17.5 tok/s, Runs well

7.7 GB required11.5 GB available

67% VRAM used

Fit status

Runs well

Decode

17.5 tok/s

TTFT

11059 ms

Safe context

90K

Memory

7.7 GB / 11.5 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsMamba Codestral 7B v0.1 on MacBook Air M2 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: 17.5 tok/s decode · 11.1s TTFT (warm) · 44 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	17.5 tok/s	6032 ms	90K
Coding	C	Runs well	17.5 tok/s	11059 ms	90K
Agentic Coding	C	Runs well	17.5 tok/s	16086 ms	90K
Reasoning	C	Runs well	17.5 tok/s	13070 ms	90K
RAG	C	Runs well	17.5 tok/s	20108 ms	90K

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 Air M2 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C49
Q3_K_S	3	3.4 GB	Low	C50
NVFP4	4	3.9 GB	Medium	C51
Q4_K_M	4	4.3 GB	Medium	C51
Q5_K_M	5	5.0 GB	High	C52
Q6_K	6	5.7 GB	High	C52
Q8_0Best for your GPU	8	7.5 GB	Very High	C51
F16	16	14.3 GB	Maximum	F0

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

Upgrade-Optionen

Hardware, die Mamba Codestral 7B v0.1 gut ausführt

MacBook Pro M3 Pro 18GBGünstige Wahl

18 GB Unified (+2)150 GB/s (+50)

Raises estimated decode speed by about 69%.29.5 tok/s Dekodierung

Raises estimated decode speed by about 69%.

ca. $1,999 MSRP

MacBook Pro M4 Pro 24GBBestes Preis-Leistungs-Verhältnis

24 GB Unified (+8)273 GB/s (+173)

Raises estimated decode speed by about 223%.56.6 tok/s Dekodierung

Raises estimated decode speed by about 223%.

Adds memory headroom for longer context windows and future model growth.

ca. $1,999 MSRP

Frequently asked questions

Can MacBook Air M2 16GB run Mamba Codestral 7B v0.1?

Yes, MacBook Air M2 16GB can run Mamba Codestral 7B v0.1 with a C grade (Runs well). Expected decode speed: 17.5 tok/s.

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

Mamba Codestral 7B v0.1 (7B parameters) requires approximately 7.7 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 Air M2 16GB?

On MacBook Air M2 16GB, Mamba Codestral 7B v0.1 achieves approximately 17.5 tokens per second decode speed with a time-to-first-token of 11059ms using Q4_K_M quantization.

Can MacBook Air M2 16GB run Mamba Codestral 7B v0.1 for coding?

For coding workloads, Mamba Codestral 7B v0.1 on MacBook Air M2 16GB receives a C grade with 17.5 tok/s and 90K context.

What context window can Mamba Codestral 7B v0.1 use on MacBook Air M2 16GB?

On MacBook Air M2 16GB, Mamba Codestral 7B v0.1 can safely use up to 90K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Air M2 16GB as fast as VRAM for Mamba Codestral 7B v0.1?

Not always. MacBook Air M2 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 Air M2 16GB See all hardware for Mamba Codestral 7B v0.1

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