Intel Arc B580 12GBコスパ重視456 GB/s (+256)
C
Raises estimated decode speed by about 68%.58.9 tok/sデコード
Raises estimated decode speed by about 68%.
〜$249 MSRP
Can Mamba Codestral 7B v0.1 run on MacBook Pro M1 Pro 16GB?
YES — Runs Great
C53Usable
Estimated from fit model
Mamba Codestral 7B v0.1 needs ~7.7 GB VRAM. MacBook Pro M1 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~35 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, 35.0 tok/s, Runs well
7.7 GB required11.5 GB available
67% VRAM used
Fit status
Runs well
Decode
35.0 tok/s
TTFT
5530 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 Pro M1 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: 35.0 tok/s decode · 5.5s TTFT (warm) · 88 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 | 35.0 tok/s | 3016 ms | 90K |
| Coding | C | Runs well | 35.0 tok/s | 5530 ms | 90K |
| Agentic Coding | C | Runs well | 35.0 tok/s | 8043 ms | 90K |
| Reasoning | C | Runs well | 35.0 tok/s | 6535 ms | 90K |
| RAG | C | Runs well | 35.0 tok/s | 10054 ms | 90K |
Quantization options
How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on MacBook Pro M1 Pro 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アップグレードオプション
Mamba Codestral 7B v0.1を快適に動かすハードウェア
Frequently asked questions
Can MacBook Pro M1 Pro 16GB run Mamba Codestral 7B v0.1?
Yes, MacBook Pro M1 Pro 16GB can run Mamba Codestral 7B v0.1 with a C grade (Runs well). Expected decode speed: 35.0 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 Pro M1 Pro 16GB?
On MacBook Pro M1 Pro 16GB, Mamba Codestral 7B v0.1 achieves approximately 35.0 tokens per second decode speed with a time-to-first-token of 5530ms using Q4_K_M quantization.
Can MacBook Pro M1 Pro 16GB run Mamba Codestral 7B v0.1 for coding?
For coding workloads, Mamba Codestral 7B v0.1 on MacBook Pro M1 Pro 16GB receives a C grade with 35.0 tok/s and 90K context.
What context window can Mamba Codestral 7B v0.1 use on MacBook Pro M1 Pro 16GB?
On MacBook Pro M1 Pro 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 Pro M1 Pro 16GB as fast as VRAM for Mamba Codestral 7B v0.1?
Not always. MacBook Pro M1 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.
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