MacBook Pro M4 Pro 64GBBudget pick
64 GB Unified (+32)
C
Raises estimated decode speed by about 36%.21.5 tok/s decode
Raises estimated decode speed by about 36%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Mistral Small 3.2 24B Instruct 2506 needs ~21.8 GB VRAM. MacBook Pro M2 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~16 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
15.8 tok/s
TTFT
12217 ms
Safe context
23K
Memory
21.8 GB / 23.0 GB
This setup is broadly balanced for this model.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 15.8 tok/s | 6664 ms | 23K |
| Coding | C | Tight fit | 15.8 tok/s | 12217 ms | 23K |
| Agentic Coding | D | Runs with offload (needs ~0.9 GB host RAM) | 14.1 tok/s | 19993 ms | 23K |
| Reasoning | C | Tight fit | 15.8 tok/s | 14438 ms | 23K |
| RAG | D | Runs with offload (needs ~0.9 GB host RAM) | 14.1 tok/s | 24991 ms |
How Mistral Small 3.2 24B Instruct 2506 (24B params) fits at each quantization level on MacBook Pro M2 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | C49 |
Q3_K_S | 3 | 11.8 GB | Low | C51 |
NVFP4 | 4 |
Copy-paste commands to run Mistral Small 3.2 24B Instruct 2506 on your machine.
Run
lms load hf-unsloth--mistral-small-3-2-24b-instruct-2506-gguf && lms server startUpgrade options
Raises estimated decode speed by about 36%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Raises estimated decode speed by about 116%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, MacBook Pro M2 Max 32GB can run Mistral Small 3.2 24B Instruct 2506 with a C grade (Tight fit). Expected decode speed: 15.8 tok/s.
Mistral Small 3.2 24B Instruct 2506 (24B parameters) requires approximately 21.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Mistral Small 3.2 24B Instruct 2506 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Max 32GB, Mistral Small 3.2 24B Instruct 2506 achieves approximately 15.8 tokens per second decode speed with a time-to-first-token of 12217ms using Q4_K_M quantization.
For coding workloads, Mistral Small 3.2 24B Instruct 2506 on MacBook Pro M2 Max 32GB receives a C grade with 15.8 tok/s and 23K context.
On MacBook Pro M2 Max 32GB, Mistral Small 3.2 24B Instruct 2506 can safely use up to 23K 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-unsloth--mistral-small-3-2-24b-instruct-2506-gguf-on-m2-max-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 23K |
13.4 GB |
| Medium |
| C50 |
Q4_K_M | 4 | 14.6 GB | Medium | C50 |
Q5_K_MBest for your GPU | 5 | 17.3 GB | High | C50 |
Q6_K | 6 | 19.7 GB | High | F0 |
Q8_0 | 8 | 25.7 GB | Very High | F0 |
F16 | 16 | 49.2 GB | Maximum | F0 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Not always. MacBook Pro M2 Max 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.