Pixtral 12B needs ~17.6 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~63 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
68.1 tok/s
TTFT
2841 ms
Safe context
131K
Memory
17.6 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 | 63.4 tok/s | 1666 ms | 131K |
| Coding | A | Runs well | 63.4 tok/s | 3054 ms | 131K |
| Agentic Coding | A | Runs well | 63.4 tok/s | 4442 ms | 131K |
| Reasoning | A | Runs well | 63.4 tok/s | 3610 ms | 131K |
| RAG | A | Runs well | 63.4 tok/s | 5553 ms | 131K |
How Pixtral 12B (12B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | B65 |
Q3_K_S | 3 | 5.9 GB | Low | B66 |
NVFP4 | 4 |
Copy-paste commands to run Pixtral 12B on your machine.
Run
ollama run pixtralYour 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 Pixtral 12B with a A grade (Runs well). Expected decode speed: 63.4 tok/s.
Pixtral 12B (12B parameters) requires approximately 17.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Pixtral 12B is Q4_K_M, which balances quality and memory efficiency.
On Mac Studio M2 Ultra 64GB, Pixtral 12B achieves approximately 63.4 tokens per second decode speed with a time-to-first-token of 3054ms using Q4_K_M quantization.
For coding workloads, Pixtral 12B on Mac Studio M2 Ultra 64GB receives a A grade with 63.4 tok/s and 131K context.
On Mac Studio M2 Ultra 64GB, Pixtral 12B 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/pixtral-12b-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:
6.7 GB |
| Medium |
| B66 |
Q4_K_M | 4 | 7.3 GB | Medium | B66 |
Q5_K_M | 5 | 8.6 GB | High | B66 |
Q6_K | 6 | 9.8 GB | High | B67 |
Q8_0 | 8 | 12.8 GB | Very High | B67 |
F16Best for your GPU | 16 | 24.6 GB | Maximum | A72 |
| Qwen 3.6 27B | 27B | S | 23.1 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.