Nemotron Nano 9B v2 needs ~10.6 GB VRAM. MacBook Air M1 16GB has 11.5 GB. With Q4_K_M quantization, expect ~7 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
8.0 tok/s
TTFT
24233 ms
Safe context
22K
Memory
10.6 GB / 11.5 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
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.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
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 | 8.0 tok/s | 13218 ms | 22K |
| Coding | A | Tight fit | 7.4 tok/s | 26051 ms | 22K |
| Agentic Coding | B | Very compromised (needs ~0.6 GB host RAM) | 6.6 tok/s | 42771 ms | 22K |
| Reasoning | A | Tight fit | 8.0 tok/s | 28639 ms | 22K |
| RAG | B | Very compromised (needs ~0.6 GB host RAM) | 6.6 tok/s | 53464 ms |
How Nemotron Nano 9B v2 (9B params) fits at each quantization level on MacBook Air M1 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 |
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 |
|---|---|---|---|---|
| 14B | B | 4 tok/s | ||
| 14B | B | 4 tok/s |
Yes, MacBook Air M1 16GB can run Nemotron Nano 9B v2 with a A grade (Tight fit). Expected decode speed: 7.4 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 Air M1 16GB, Nemotron Nano 9B v2 achieves approximately 7.4 tokens per second decode speed with a time-to-first-token of 26051ms using Q4_K_M quantization.
For coding workloads, Nemotron Nano 9B v2 on MacBook Air M1 16GB receives a A grade with 7.4 tok/s and 22K context.
On MacBook Air M1 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/nemotron-nano-9b-v2-on-m1-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 22K |
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 |
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Not always. MacBook Air M1 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.