Nemotron 3 Nano 30B needs ~34.4 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~110 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
118.4 tok/s
TTFT
1635 ms
Safe context
131K
Memory
34.4 GB / 128.0 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 | S | Runs well | 118.4 tok/s | 892 ms | 131K |
| Coding | S | Runs well | 110.2 tok/s | 1757 ms | 131K |
| Agentic Coding | S | Runs well | 118.4 tok/s | 2378 ms | 131K |
| Reasoning | S | Runs well | 118.4 tok/s | 1932 ms | 131K |
| RAG | S | Runs well | 118.4 tok/s | 2972 ms | 131K |
How Nemotron 3 Nano 30B (30B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.7 GB | Low | A79 |
Q3_K_S | 3 | 14.7 GB | Low | A79 |
NVFP4 | 4 |
Copy-paste commands to run Nemotron 3 Nano 30B on your machine.
Run
ollama run nemotron-nano:30bYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Devstral 2 123B Instruct | 123B | S | 29.2 tok/s | |
 Qwen3-Coder 30B A3B Instruct | 30.5B | S |
Yes, Intel Data Center GPU Max 1550 128GB can run Nemotron 3 Nano 30B with a S grade (Runs well). Expected decode speed: 110.2 tok/s.
Nemotron 3 Nano 30B (30B parameters) requires approximately 34.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Nemotron 3 Nano 30B is Q4_K_M, which balances quality and memory efficiency.
On Intel Data Center GPU Max 1550 128GB, Nemotron 3 Nano 30B achieves approximately 110.2 tokens per second decode speed with a time-to-first-token of 1757ms using Q4_K_M quantization.
For coding workloads, Nemotron 3 Nano 30B on Intel Data Center GPU Max 1550 128GB receives a S grade with 110.2 tok/s and 131K context.
On Intel Data Center GPU Max 1550 128GB, Nemotron 3 Nano 30B can safely use up to 131K 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-3-nano-30b-on-max-1550-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
16.8 GB |
| Medium |
| A79 |
Q4_K_M | 4 | 18.3 GB | Medium | A79 |
Q5_K_M | 5 | 21.6 GB | High | A79 |
Q6_K | 6 | 24.6 GB | High | A80 |
Q8_0 | 8 | 32.1 GB | Very High | A81 |
F16Best for your GPU | 16 | 61.5 GB | Maximum | S86 |
| 304.8 tok/s |
| Qwen 3.5 122B A10B | 122B | S | 81 tok/s |
| Qwen 3.6 35B A3B | 35B | S | 256.2 tok/s |
| Qwen 3.5 35B A3B | 35B | S | 278.6 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.