Codestral 2 25.08 needs ~20.2 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~9 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
9.1 tok/s
TTFT
21256 ms
Safe context
34K
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 | A | Tight fit | 9.1 tok/s | 11594 ms | 34K |
| Coding | A | Tight fit | 9.1 tok/s | 21256 ms | 34K |
| Agentic Coding | A | Runs with offload | 9.1 tok/s | 30918 ms | 34K |
| Reasoning | A | Tight fit | 9.1 tok/s | 25121 ms | 34K |
| RAG | A | Runs with offload | 9.1 tok/s | 38647 ms | 34K |
How Codestral 2 25.08 (22B params) fits at each quantization level on MacBook Pro M4 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 8.6 GB | Low | A83 |
Q3_K_S | 3 | 10.8 GB | Low | A84 |
NVFP4 | 4 | 12.3 GB | Medium | A85 |
Q4_K_M | 4 | 13.4 GB | Medium | A84 |
Q5_K_M | 5 | 15.8 GB | High | A84 |
Q6_KBest for your GPU | 6 | 18.0 GB | High | A84 |
Q8_0 | 8 | 23.5 GB | Very High | F0 |
F16 | 16 | 45.1 GB | Maximum | F0 |
Copy-paste commands to run Codestral 2 25.08 on your machine.
Run
lms load codestral-2508 && lms server startYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | A | 11.7 tok/s | |
| Qwen 3.5 27B | 27B | S | 8.6 tok/s | |
| Qwen 3.6 27B | 27B | S | 7.1 tok/s | |
| Qwen3-VL 30B A3B Instruct | 30B | S | 12.4 tok/s | |
| Qwen 3.5 35B A3B | 35B | A | 10.2 tok/s |
Yes, MacBook Pro M4 32GB can run Codestral 2 25.08 with a A grade (Tight fit). Expected decode speed: 9.1 tok/s.
Codestral 2 25.08 (22B parameters) requires approximately 20.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Codestral 2 25.08 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 32GB, Codestral 2 25.08 achieves approximately 9.1 tokens per second decode speed with a time-to-first-token of 21256ms using Q4_K_M quantization.
For coding workloads, Codestral 2 25.08 on MacBook Pro M4 32GB receives a A grade with 9.1 tok/s and 34K context.
On MacBook Pro M4 32GB, Codestral 2 25.08 can safely use up to 34K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.
Not always. MacBook Pro M4 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/codestral-2-25.08-on-m4-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: