Nemotron Nano 9B v2 needs ~10.4 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~49 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
49.3 tok/s
TTFT
3923 ms
Safe context
52K
Memory
10.4 GB / 16.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 49.3 tok/s | 2140 ms | 52K |
| Coding | A | Runs well | 49.3 tok/s | 3923 ms | 52K |
| Agentic Coding | A | Runs well | 49.3 tok/s | 5707 ms | 52K |
| Reasoning | A | Runs well | 49.3 tok/s | 4637 ms | 52K |
| RAG | A | Runs well | 49.3 tok/s | 7134 ms | 52K |
How Nemotron Nano 9B v2 (9B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | A77 |
Q3_K_S | 3 | 4.4 GB | Low | A78 |
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 | S | 31.9 tok/s | ||
| 14.7B | S | 30.2 tok/s |
Yes, Intel Arc A770 16GB can run Nemotron Nano 9B v2 with a A grade (Runs well). Expected decode speed: 49.3 tok/s.
Nemotron Nano 9B v2 (9B parameters) requires approximately 10.4 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 Intel Arc A770 16GB, Nemotron Nano 9B v2 achieves approximately 49.3 tokens per second decode speed with a time-to-first-token of 3923ms using Q4_K_M quantization.
For coding workloads, Nemotron Nano 9B v2 on Intel Arc A770 16GB receives a A grade with 49.3 tok/s and 52K context.
On Intel Arc A770 16GB, Nemotron Nano 9B v2 can safely use up to 52K 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-arc-a770-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
5.0 GB |
| Medium |
| A78 |
Q4_K_M | 4 | 5.5 GB | Medium | A79 |
Q5_K_M | 5 | 6.5 GB | High | A80 |
Q6_K | 6 | 7.4 GB | High | A81 |
Q8_0Best for your GPU | 8 | 9.6 GB | Very High | A81 |
F16 | 16 | 18.5 GB | Maximum | F0 |
| 21B | A | 29.2 tok/s |
| 14B | S | 31.7 tok/s |
| 22B | A | 10.7 tok/s |
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.