Raises estimated decode speed by about 111%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Codestral 22B needs ~20.2 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~10 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
Tight fit
Decode
11.2 tok/s
TTFT
17263 ms
Safe context
33K
Memory
20.2 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 | B | Tight fit | 10.4 tok/s | 10123 ms | 33K |
| Coding | B | Tight fit | 10.4 tok/s | 18558 ms | 33K |
| Agentic Coding | B | Runs with offload | 10.4 tok/s | 26993 ms | 33K |
| Reasoning | B | Tight fit | 10.4 tok/s | 21932 ms | 33K |
| RAG | B | Runs with offload | 10.4 tok/s | 33742 ms | 33K |
How Codestral 22B (22B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 8.6 GB | Low | B58 |
Q3_K_S | 3 | 10.8 GB | Low | B60 |
NVFP4 | 4 |
Copy-paste commands to run Codestral 22B on your machine.
Run
ollama run codestralUpgrade options
Raises estimated decode speed by about 111%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
~$1,999 MSRP
Raises estimated decode speed by about 166%.
~$2,499 MSRP
Yes, MacBook Pro M2 Pro 32GB can run Codestral 22B with a B grade (Tight fit). Expected decode speed: 10.4 tok/s.
Codestral 22B (22B parameters) requires approximately 20.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Codestral 22B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 32GB, Codestral 22B achieves approximately 10.4 tokens per second decode speed with a time-to-first-token of 18558ms using Q4_K_M quantization.
For coding workloads, Codestral 22B on MacBook Pro M2 Pro 32GB receives a B grade with 10.4 tok/s and 33K context.
On MacBook Pro M2 Pro 32GB, Codestral 22B can safely use up to 33K tokens of context. The model's official context limit is 33K, 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-22b-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:
12.3 GB |
| Medium |
| B60 |
Q4_K_M | 4 | 13.4 GB | Medium | B60 |
Q5_K_M | 5 | 15.8 GB | High | B60 |
Q6_KBest for your GPU | 6 | 18.0 GB | High | B60 |
Q8_0 | 8 | 23.5 GB | Very High | F0 |
F16 | 16 | 45.1 GB | Maximum | F0 |
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.