RX 7900 XTX 24GBBudget pick
960 GB/s (+560)
B
Raises estimated decode speed by about 198%.101.5 tok/s decode
Raises estimated decode speed by about 198%.
~$999 MSRP
Mistral Nemo 12B needs ~14.1 GB VRAM. MacBook Pro M2 Max 32GB has 23.0 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
74K
Memory
14.1 GB / 23.0 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 | B | Runs well | 34.1 tok/s | 3099 ms | 74K |
| Coding | B | Runs well | 34.1 tok/s | 5682 ms | 74K |
| Agentic Coding | B | Runs well | 34.1 tok/s | 8265 ms | 74K |
| Reasoning | B | Runs well | 34.1 tok/s | 6715 ms | 74K |
| RAG | B | Runs well | 34.1 tok/s | 10331 ms | 74K |
How Mistral Nemo 12B (12B params) fits at each quantization level on MacBook Pro M2 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | B58 |
Q3_K_S | 3 | 5.9 GB | Low | B59 |
NVFP4 | 4 |
Copy-paste commands to run Mistral Nemo 12B on your machine.
Run
ollama run mistral-nemoUpgrade options
Raises estimated decode speed by about 198%.
~$999 MSRP
RTX 3090 24GBBest valueRaises estimated decode speed by about 177%.
~$1,499 MSRP
Yes, MacBook Pro M2 Max 32GB can run Mistral Nemo 12B with a B grade (Runs well). Expected decode speed: 34.1 tok/s.
Mistral Nemo 12B (12B parameters) requires approximately 14.1 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 Max 32GB, Mistral Nemo 12B 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 Nemo 12B on MacBook Pro M2 Max 32GB receives a B grade with 34.1 tok/s and 74K context.
On MacBook Pro M2 Max 32GB, Mistral Nemo 12B can safely use up to 74K 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-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:
6.7 GB |
| Medium |
| B59 |
Q4_K_M | 4 | 7.3 GB | Medium | B60 |
Q5_K_M | 5 | 8.6 GB | High | B60 |
Q6_K | 6 | 9.8 GB | High | B61 |
Q8_0Best for your GPU | 8 | 12.8 GB | Very High | B63 |
F16 | 16 | 24.6 GB | Maximum | F0 |
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.