Nemotron 3 Nano 30B needs ~16.9 GB VRAM. RTX 6000 Ada Laptop 16GB has 16.0 GB. With Q2_K quantization, expect ~22 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
7.5 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
8.2 tok/s
TTFT
23560 ms
Safe context
4K
Memory
23.5 GB / 16.0 GB
Offload
30%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 0.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 9.2 tok/s | 11488 ms | 4K |
| Coding | F | Too heavy | 8.2 tok/s | 23560 ms | 4K |
| Agentic Coding | F | Too heavy | 6.7 tok/s | 42180 ms | 4K |
| Reasoning | F | Too heavy | 8.2 tok/s | 27844 ms | 4K |
| RAG | F | Too heavy | 6.7 tok/s | 52725 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for Nemotron 3 Nano 30B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~71 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 | 70.5 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 38.1 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 38.1 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 32.8 | Offloads |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 32.7 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 30.3 | Offloads |
| RTX 3090 24GB | 24 GB | Q4_K_M | 28.0 | Offloads |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 27.3 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 25.8 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 24.0 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 14.1 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 12.9 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 11.9 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 4.2 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.6 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | 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 3 Nano 30B (30B params) fits at each quantization level on RTX 6000 Ada Laptop 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.7 GB | Low | F0 |
Q3_K_S | 3 | 14.7 GB | Low | F0 |
NVFP4 | 4 | 16.8 GB | Medium | F0 |
Q4_K_M | 4 | 18.3 GB | Medium | F0 |
Q5_K_M | 5 | 21.6 GB | High | F0 |
Q6_K | 6 | 24.6 GB | High | F0 |
Q8_0 | 8 | 32.1 GB | Very High | F0 |
F16 | 16 | 61.5 GB | Maximum | F0 |
Copy-paste commands to run Nemotron 3 Nano 30B on your machine.
Run
ollama run nemotron-nano:30bUpgrade options
RTX 3090 24GBBudget pickMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,499 MSRP
RTX 4090 24GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,599 MSRP
RTX 5090 32GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,999 MSRP
Yes, RTX 6000 Ada Laptop 16GB can run Nemotron 3 Nano 30B at Q2_K quantization (Runs with offload (needs ~0.7 GB host RAM)). The recommended Q4_K_M requires 23.5 GB which exceeds available memory, but at Q2_K it needs only 16.9 GB. Expected decode speed: 21.8 tok/s.
Nemotron 3 Nano 30B (30B parameters) requires approximately 23.5 GB at Q4_K_M quantization. On RTX 6000 Ada Laptop 16GB, it fits at Q2_K using 16.9 GB.
The recommended quantization is Q4_K_M, but on RTX 6000 Ada Laptop 16GB the best fitting quantization is Q2_K, which uses 16.9 GB.
On RTX 6000 Ada Laptop 16GB, Nemotron 3 Nano 30B achieves approximately 21.8 tokens per second decode speed with a time-to-first-token of 8866ms using Q2_K quantization.
For coding workloads, Nemotron 3 Nano 30B on RTX 6000 Ada Laptop 16GB receives a F grade with 8.2 tok/s and 4K context.
On RTX 6000 Ada Laptop 16GB, Nemotron 3 Nano 30B can safely use up to 10K tokens of context at Q2_K quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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