MacBook Pro M4 Max 36GBBudget pick
36 GB Unified (+4)410 GB/s (+210)
C
Raises estimated decode speed by about 85%.69.6 tok/s decode
Raises estimated decode speed by about 85%.
~$2,499 MSRP
Mamba Codestral 7B v0.1 needs ~9.4 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~38 tok/s.
Operating mode
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
Fit status
Runs well
Decode
37.7 tok/s
TTFT
5135 ms
Safe context
281K
Memory
9.4 GB / 23.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 37.7 tok/s | 2801 ms | 281K |
| Coding | C | Runs well | 37.7 tok/s | 5135 ms | 281K |
| Agentic Coding | C | Runs well | 37.7 tok/s | 7469 ms | 281K |
| Reasoning | C | Runs well | 37.7 tok/s | 6068 ms | 281K |
| RAG | C | Runs well | 37.7 tok/s | 9336 ms | 281K |
How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C44 |
Q3_K_S | 3 | 3.4 GB | Low | C44 |
NVFP4 | 4 |
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 options
Raises estimated decode speed by about 85%.
~$2,499 MSRP
Raises estimated decode speed by about 146%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 160%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, MacBook Pro M2 Pro 32GB can run Mamba Codestral 7B v0.1 with a C grade (Runs well). Expected decode speed: 37.7 tok/s.
Mamba Codestral 7B v0.1 (7B parameters) requires approximately 9.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Mamba Codestral 7B v0.1 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 32GB, Mamba Codestral 7B v0.1 achieves approximately 37.7 tokens per second decode speed with a time-to-first-token of 5135ms using Q4_K_M quantization.
For coding workloads, Mamba Codestral 7B v0.1 on MacBook Pro M2 Pro 32GB receives a C grade with 37.7 tok/s and 281K context.
On MacBook Pro M2 Pro 32GB, Mamba Codestral 7B v0.1 can safely use up to 281K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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-pro-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| C45 |
Q4_K_M | 4 | 4.3 GB | Medium | C45 |
Q5_K_M | 5 | 5.0 GB | High | C45 |
Q6_K | 6 | 5.7 GB | High | C46 |
Q8_0 | 8 | 7.5 GB | Very High | C47 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C50 |
Not always. MacBook Pro M2 Pro 32GB 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.