Mistral Small 3.2 24B needs ~25.2 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~34 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
Runs well
Decode
34.1 tok/s
TTFT
5682 ms
Safe context
131K
Memory
25.2 GB / 46.1 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 | Runs well | 34.1 tok/s | 3099 ms | 131K |
| Coding | A | Runs well | 34.1 tok/s | 5682 ms | 131K |
| Agentic Coding | S | Runs well | 31.7 tok/s | 8885 ms | 131K |
| Reasoning | A | Runs well | 34.1 tok/s | 6715 ms | 131K |
| RAG | S | Runs well | 34.1 tok/s | 10331 ms | 131K |
How Mistral Small 3.2 24B (24B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | A77 |
Q3_K_S | 3 | 11.8 GB | Low | A78 |
NVFP4 | 4 |
Copy-paste commands to run Mistral Small 3.2 24B on your machine.
Run
ollama run mistral-small3.2Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | S | 70.2 tok/s | |
| Qwen 3.5 27B | 27B | S | 30.4 tok/s |
Yes, Mac Studio M2 Ultra 64GB can run Mistral Small 3.2 24B with a A grade (Runs well). Expected decode speed: 34.1 tok/s.
Mistral Small 3.2 24B (24B parameters) requires approximately 25.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Mistral Small 3.2 24B is Q4_K_M, which balances quality and memory efficiency.
On Mac Studio M2 Ultra 64GB, Mistral Small 3.2 24B achieves approximately 34.1 tokens per second decode speed with a time-to-first-token of 5682ms using Q4_K_M quantization.
For coding workloads, Mistral Small 3.2 24B on Mac Studio M2 Ultra 64GB receives a A grade with 34.1 tok/s and 131K context.
On Mac Studio M2 Ultra 64GB, Mistral Small 3.2 24B can safely use up to 131K tokens of context. The model's official context limit is 131K, 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/mistral-small-3.2-24b-on-m2-ultra-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
13.4 GB |
| Medium |
| A78 |
Q4_K_M | 4 | 14.6 GB | Medium | A79 |
Q5_K_M | 5 | 17.3 GB | High | A80 |
Q6_K | 6 | 19.7 GB | High | A80 |
Q8_0Best for your GPU | 8 | 25.7 GB | Very High | A82 |
F16 | 16 | 49.2 GB | Maximum | F0 |
| Qwen 3.6 27B | 27B | S | 30.5 tok/s |
| Qwen 3.6 35B A3B | 35B | S | 59 tok/s |
| Qwen3-VL 30B A3B Instruct | 30B | S | 72.6 tok/s |
Not always. Mac Studio M2 Ultra 64GB 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.