Codestral 22B v0.1 needs ~20.4 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~6 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
9.0 tok/s
TTFT
21479 ms
Safe context
33K
Memory
20.4 GB / 23.0 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
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.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 6.4 tok/s | 16402 ms | 33K |
| Coding | C | Tight fit | 6.4 tok/s | 30071 ms | 33K |
| Agentic Coding | C | Runs with offload | 6.4 tok/s | 43739 ms | 33K |
| Reasoning | C | Tight fit | 9.0 tok/s | 25384 ms | 33K |
| RAG | C | Runs with offload | 9.0 tok/s | 39053 ms | 33K |
How Codestral 22B v0.1 (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 | C48 |
Q3_K_S | 3 | 10.8 GB | Low | C50 |
NVFP4 | 4 |
Copy-paste commands to run Codestral 22B v0.1 on your machine.
Run
lms load hf-lmstudio-community--codestral-22b-v0-1-gguf && lms server startUpgrade options
Raises estimated decode speed by about 143%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
~$1,999 MSRP
Raises estimated decode speed by about 209%.
~$2,499 MSRP
Yes, MacBook Pro M4 32GB can run Codestral 22B v0.1 with a C grade (Tight fit). Expected decode speed: 6.4 tok/s.
Codestral 22B v0.1 (22B parameters) requires approximately 20.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Codestral 22B v0.1 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 32GB, Codestral 22B v0.1 achieves approximately 6.4 tokens per second decode speed with a time-to-first-token of 30071ms using Q4_K_M quantization.
For coding workloads, Codestral 22B v0.1 on MacBook Pro M4 32GB receives a C grade with 6.4 tok/s and 33K context.
On MacBook Pro M4 32GB, Codestral 22B v0.1 can safely use up to 33K 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-lmstudio-community--codestral-22b-v0-1-gguf-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:
12.3 GB |
| Medium |
| C50 |
Q4_K_M | 4 | 13.4 GB | Medium | C50 |
Q5_K_M | 5 | 15.8 GB | High | C50 |
Q6_KBest for your GPU | 6 | 18.0 GB | High | C49 |
Q8_0 | 8 | 23.5 GB | Very High | F0 |
F16 | 16 | 45.1 GB | Maximum | F0 |
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
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.