RX 7900 XT 20GBBudget pick
800 GB/s (+527)
B
Raises estimated decode speed by about 159%.70.5 tok/s decode
Raises estimated decode speed by about 159%.
~$899 MSRP
Mistral Nemo 12B needs ~13.3 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~27 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
27.2 tok/s
TTFT
7126 ms
Safe context
42K
Memory
13.3 GB / 17.3 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 | 27.2 tok/s | 3887 ms | 42K |
| Coding | B | Runs well | 27.2 tok/s | 7126 ms | 42K |
| Agentic Coding | B | Tight fit | 27.2 tok/s | 10366 ms | 42K |
| Reasoning | B | Runs well | 27.2 tok/s | 8422 ms | 42K |
| RAG | B | Tight fit | 27.2 tok/s | 12957 ms | 42K |
How Mistral Nemo 12B (12B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | B60 |
Q3_K_S | 3 | 5.9 GB | Low | B61 |
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 159%.
~$899 MSRP
Raises estimated decode speed by about 273%.
Adds memory headroom for longer context windows and future model growth.
~$999 MSRP
Yes, MacBook Pro M4 Pro 24GB can run Mistral Nemo 12B with a B grade (Runs well). Expected decode speed: 27.2 tok/s.
Mistral Nemo 12B (12B parameters) requires approximately 13.3 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 M4 Pro 24GB, Mistral Nemo 12B achieves approximately 27.2 tokens per second decode speed with a time-to-first-token of 7126ms using Q4_K_M quantization.
For coding workloads, Mistral Nemo 12B on MacBook Pro M4 Pro 24GB receives a B grade with 27.2 tok/s and 42K context.
On MacBook Pro M4 Pro 24GB, Mistral Nemo 12B can safely use up to 42K 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-m4-pro-24gb" 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 |
| B62 |
Q4_K_M | 4 | 7.3 GB | Medium | B62 |
Q5_K_M | 5 | 8.6 GB | High | B64 |
Q6_K | 6 | 9.8 GB | High | B63 |
Q8_0Best for your GPU | 8 | 12.8 GB | Very High | B63 |
F16 | 16 | 24.6 GB | Maximum | F0 |
Not always. MacBook Pro M4 Pro 24GB 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.