Nemotron Nano 9B v2 needs ~10.6 GB VRAM. MacBook Pro M1 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~26 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
Tight fit
Decode
25.5 tok/s
TTFT
7605 ms
Safe context
22K
Memory
10.6 GB / 11.5 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 | Runs well | 25.5 tok/s | 4148 ms | 22K |
| Coding | A | Tight fit | 25.5 tok/s | 7605 ms | 22K |
| Agentic Coding | B | Very compromised (needs ~0.6 GB host RAM) | 21.0 tok/s | 13423 ms | 22K |
| Reasoning | A | Tight fit | 25.5 tok/s | 8988 ms | 22K |
| RAG | B | Very compromised (needs ~0.6 GB host RAM) | 21.0 tok/s | 16779 ms | 22K |
How Nemotron Nano 9B v2 (9B params) fits at each quantization level on MacBook Pro M1 Pro 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | A80 |
Q3_K_S | 3 | 4.4 GB | Low | A81 |
NVFP4 | 4 | 5.0 GB | Medium | A82 |
Q4_K_M | 4 | 5.5 GB | Medium | A82 |
Q5_K_M | 5 | 6.5 GB | High | A82 |
Q6_KBest for your GPU | 6 | 7.4 GB | High | A81 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Copy-paste commands to run Nemotron Nano 9B v2 on your machine.
Run
ollama run nemotron-nano:9b-v2Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3 14B | 14B | A | 12.8 tok/s | |
 Ministral 3 14B | 14B | B | 12.7 tok/s |
Yes, MacBook Pro M1 Pro 16GB can run Nemotron Nano 9B v2 with a A grade (Tight fit). Expected decode speed: 25.5 tok/s.
Nemotron Nano 9B v2 (9B parameters) requires approximately 10.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Nemotron Nano 9B v2 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M1 Pro 16GB, Nemotron Nano 9B v2 achieves approximately 25.5 tokens per second decode speed with a time-to-first-token of 7605ms using Q4_K_M quantization.
For coding workloads, Nemotron Nano 9B v2 on MacBook Pro M1 Pro 16GB receives a A grade with 25.5 tok/s and 22K context.
On MacBook Pro M1 Pro 16GB, Nemotron Nano 9B v2 can safely use up to 22K 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 M1 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/nemotron-nano-9b-v2-on-m1-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: