Codestral Mamba 7B needs ~12.6 GB VRAM. MacBook Pro M3 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~65 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
64.6 tok/s
TTFT
2995 ms
Safe context
262K
Memory
12.6 GB / 46.1 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 | A | Runs well | 64.6 tok/s | 1634 ms | 262K |
| Coding | A | Runs well | 64.6 tok/s | 2995 ms | 262K |
| Agentic Coding | A | Runs well | 64.6 tok/s | 4357 ms | 262K |
| Reasoning | A | Runs well | 64.6 tok/s | 3540 ms | 262K |
| RAG | A | Runs well | 64.6 tok/s | 5446 ms | 262K |
How Codestral Mamba 7B (7B params) fits at each quantization level on MacBook Pro M3 Max 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B67 |
Q3_K_S | 3 | 3.4 GB | Low | B67 |
NVFP4 | 4 |
Copy-paste commands to run Codestral Mamba 7B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "mistralai/Mamba-Codestral-7B-v0.1" \
--hf-file "Mamba-Codestral-7B-v0.1-Q4_K_M.gguf" \
-c 4096 -ngl 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 30.5B | S | 36.3 tok/s | ||
| 27B | S | 15.7 tok/s |
Yes, MacBook Pro M3 Max 64GB can run Codestral Mamba 7B with a A grade (Runs well). Expected decode speed: 64.6 tok/s.
Codestral Mamba 7B (7B parameters) requires approximately 12.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Codestral Mamba 7B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Max 64GB, Codestral Mamba 7B achieves approximately 64.6 tokens per second decode speed with a time-to-first-token of 2995ms using Q4_K_M quantization.
For coding workloads, Codestral Mamba 7B on MacBook Pro M3 Max 64GB receives a A grade with 64.6 tok/s and 262K context.
On MacBook Pro M3 Max 64GB, Codestral Mamba 7B can safely use up to 262K tokens of context. The model's official context limit is 262K, 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/codestral-mamba-7b-on-m3-max-64gb" 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 |
| B67 |
Q4_K_M | 4 | 4.3 GB | Medium | B67 |
Q5_K_M | 5 | 5.0 GB | High | B67 |
Q6_K | 6 | 5.7 GB | High | B67 |
Q8_0 | 8 | 7.5 GB | Very High | B68 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | B69 |
| 27B | S | 12 tok/s |
| 35B | S | 33.5 tok/s |
| 30B | S | 37.5 tok/s |
Not always. MacBook Pro M3 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.