MacBook Pro M3 Pro 18GBGünstige Wahl
18 GB Unified (+2)150 GB/s (+50)
C
Raises estimated decode speed by about 69%.29.5 tok/s Dekodierung
Raises estimated decode speed by about 69%.
ca. $1,999 MSRP
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.
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 |
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 startUpgrade-Optionen
Hardware, die Mamba Codestral 7B v0.1 gut ausführt
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.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-gabriellarson--mamba-codestral-7b-v0-1-gguf-on-m2-air-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: