Codestral 2 25.08 needs ~19.4 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~18 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
2.1 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~1.4 GB host RAM)
Decode
18.4 tok/s
TTFT
10494 ms
Safe context
4K
Memory
19.4 GB / 17.3 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 1.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs with offload (needs ~0.6 GB host RAM) | 20.2 tok/s | 5218 ms | 4K |
| Coding | A | Very compromised (needs ~1.4 GB host RAM) | 18.4 tok/s | 10494 ms | 4K |
| Agentic Coding | F | Too heavy | 15.9 tok/s | 17744 ms | 4K |
| Reasoning | A | Very compromised | 12.3 tok/s | 18665 ms | 4K |
| RAG | F | Too heavy | 15.9 tok/s | 22179 ms |
How Codestral 2 25.08 (22B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 8.6 GB | Low | S85 |
Q3_K_S | 3 | 10.8 GB | Low | S85 |
NVFP4Best for your GPU |
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 |
|---|---|---|---|---|
 Magistral Small 2507 | 24B | A | 17.8 tok/s | |
| Devstral Small 2 24B Instruct | 24B | A | 17.8 tok/s |
Yes, MacBook Pro M4 Pro 24GB can run Codestral 2 25.08 with a A grade (Very compromised (needs ~1.4 GB host RAM)). Expected decode speed: 18.4 tok/s.
Codestral 2 25.08 (22B parameters) requires approximately 19.4 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 Pro 24GB, Codestral 2 25.08 achieves approximately 18.4 tokens per second decode speed with a time-to-first-token of 10494ms using Q4_K_M quantization.
For coding workloads, Codestral 2 25.08 on MacBook Pro M4 Pro 24GB receives a A grade with 18.4 tok/s and 4K context.
On MacBook Pro M4 Pro 24GB, Codestral 2 25.08 can safely use up to 4K tokens of context. The model's official context limit is 256K, 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-2-25.08-on-m4-pro-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4K |
| 4 |
12.3 GB |
| Medium |
| A85 |
Q4_K_M | 4 | 13.4 GB | Medium | F0 |
Q5_K_M | 5 | 15.8 GB | High | F0 |
Q6_K | 6 | 18.0 GB | High | F0 |
Q8_0 | 8 | 23.5 GB | Very High | F0 |
F16 | 16 | 45.1 GB | Maximum | F0 |
| Devstral Small 1.1 | 24B | A | 17.8 tok/s |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Not always. MacBook Pro M4 Pro 24GB 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.