Mistral Nemo 12B needs ~12.4 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~18 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
0.9 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.5 GB host RAM)
Decode
18.1 tok/s
TTFT
10693 ms
Safe context
10K
Memory
12.4 GB / 11.5 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 0.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs with offload | 20.6 tok/s | 5136 ms | 10K |
| Coding | C | Runs with offload (needs ~0.5 GB host RAM) | 18.1 tok/s | 10693 ms | 10K |
| Agentic Coding | F | Too heavy | 14.4 tok/s | 19603 ms | 10K |
| Reasoning | C | Runs with offload (needs ~0.5 GB host RAM) | 18.1 tok/s | 12637 ms | 10K |
| RAG | F | Too heavy | 14.4 tok/s | 24504 ms |
How Mistral Nemo 12B (12B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | B64 |
Q3_K_S | 3 | 5.9 GB | Low | B65 |
NVFP4 | 4 |
Copy-paste commands to run Mistral Nemo 12B on your machine.
Run
ollama run mistral-nemoUpgrade options
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
RTX 4080 Super 16GBBiggest leapRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 420%.
~$999 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Yes, MacBook Pro M2 Pro 16GB can run Mistral Nemo 12B with a C grade (Runs with offload (needs ~0.5 GB host RAM)). Expected decode speed: 18.1 tok/s.
Mistral Nemo 12B (12B parameters) requires approximately 12.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Mistral Nemo 12B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 16GB, Mistral Nemo 12B achieves approximately 18.1 tokens per second decode speed with a time-to-first-token of 10693ms using Q4_K_M quantization.
For coding workloads, Mistral Nemo 12B on MacBook Pro M2 Pro 16GB receives a C grade with 18.1 tok/s and 10K context.
On MacBook Pro M2 Pro 16GB, Mistral Nemo 12B can safely use up to 10K tokens of context. The model's official context limit is 128K, 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-nemo-12b-on-m2-pro-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 10K |
6.7 GB |
| Medium |
| B64 |
Q4_K_MBest for your GPU | 4 | 7.3 GB | Medium | B64 |
Q5_K_M | 5 | 8.6 GB | High | F0 |
Q6_K | 6 | 9.8 GB | High | F0 |
Q8_0 | 8 | 12.8 GB | Very High | F0 |
F16 | 16 | 24.6 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Not always. MacBook Pro M2 Pro 16GB 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.