Nemotron Nano 9B v2 needs ~10.6 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 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
Tight fit
Decode
27.4 tok/s
TTFT
7062 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 | 27.4 tok/s | 3852 ms | 22K |
| Coding | A | Tight fit | 27.4 tok/s | 7062 ms | 22K |
| Agentic Coding | B | Very compromised (needs ~0.6 GB host RAM) | 22.6 tok/s | 12465 ms | 22K |
| Reasoning | A | Tight fit | 27.4 tok/s | 8346 ms | 22K |
| RAG | B | Very compromised (needs ~0.6 GB host RAM) | 22.6 tok/s | 15581 ms | 22K |
Inference speed
Estimated decode speed (tokens/sec) for Nemotron Nano 9B v2 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 126.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 126.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 126.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 126.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 119.6 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 109.1 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 90.9 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 86.2 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 79.3 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 79.3 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 74.0 | Tight |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 47.0 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 46.5 | Tight |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 43.1 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 40.9 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 18.5 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Nemotron Nano 9B v2 (9B params) fits at each quantization level on MacBook Pro M2 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 | 13.8 tok/s | |
 Ministral 3 14B | 14B | B | 13.7 tok/s |
Yes, MacBook Pro M2 Pro 16GB can run Nemotron Nano 9B v2 with a A grade (Tight fit). Expected decode speed: 27.4 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 Pro M2 Pro 16GB, Nemotron Nano 9B v2 achieves approximately 27.4 tokens per second decode speed with a time-to-first-token of 7062ms using Q4_K_M quantization.
For coding workloads, Nemotron Nano 9B v2 on MacBook Pro M2 Pro 16GB receives a A grade with 27.4 tok/s and 22K context.
On MacBook Pro M2 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 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/nemotron-nano-9b-v2-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>
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