Pixtral 12B needs ~12.6 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 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
Runs with offload
Decode
16.1 tok/s
TTFT
12039 ms
Safe context
18K
Memory
12.6 GB / 13.0 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 | A | Tight fit | 16.1 tok/s | 6567 ms | 18K |
| Coding | A | Runs with offload | 16.1 tok/s | 12039 ms | 18K |
| Agentic Coding | B | Very compromised (needs ~1 GB host RAM) | 12.8 tok/s | 22052 ms | 18K |
| Reasoning | A | Runs with offload | 16.1 tok/s | 14228 ms | 18K |
| RAG | B | Very compromised (needs ~1 GB host RAM) | 12.8 tok/s | 27564 ms | 18K |
How Pixtral 12B (12B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | A74 |
Q3_K_S | 3 | 5.9 GB | Low | A76 |
NVFP4 | 4 | 6.7 GB | Medium | A76 |
Q4_K_M | 4 | 7.3 GB | Medium | A75 |
Q5_K_M | 5 | 8.6 GB | High | A75 |
Q6_KBest for your GPU | 6 | 9.8 GB | High | A75 |
Q8_0 | 8 | 12.8 GB | Very High | F0 |
F16 | 16 | 24.6 GB | Maximum | F0 |
Copy-paste commands to run Pixtral 12B on your machine.
Run
ollama run pixtralYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3 14B | 14B | A | 12.3 tok/s | |
 Phi-4-reasoning-plus 14B | 14.7B | A | 10.6 tok/s | |
 Ministral 3 14B | 14B | A | 12.3 tok/s | |
| Phi-4 14B | 14B | B | 11.6 tok/s | |
| Qwen 2.5 14B | 14B | B | 11.7 tok/s |
Yes, MacBook Pro M3 Pro 18GB can run Pixtral 12B with a A grade (Runs with offload). Expected decode speed: 16.1 tok/s.
Pixtral 12B (12B parameters) requires approximately 12.6 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 MacBook Pro M3 Pro 18GB, Pixtral 12B achieves approximately 16.1 tokens per second decode speed with a time-to-first-token of 12039ms using Q4_K_M quantization.
For coding workloads, Pixtral 12B on MacBook Pro M3 Pro 18GB receives a A grade with 16.1 tok/s and 18K context.
On MacBook Pro M3 Pro 18GB, Pixtral 12B can safely use up to 18K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
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 M3 Pro 18GB 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/pixtral-12b-on-m3-pro-18gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: