ToolSite Nemotron 3 Nano 30B-A3B VRAM requirements
Nemotron 3 Nano 30B-A3B has 31.6B parameters, of which about 3.5B are used per token; all 128 experts still have to be in memory. With an 8K-token context and one request it needs about 20.1 GB of GPU memory at Q4_K_M, 32.9 GB at FP8 and 65.3 GB at FP16/BF16. The published weights take 58.8 GB (BF16). On one 24 GB RTX 3090 / 4090 it runs at Q4_K_M with its full 256K-token context.
Open Nemotron 3 Nano 30B-A3B in the calculator
VRAM by quantization
Weights plus the KV cache for 8,192 tokens in FP16 and the runtime overhead (0.5 GB plus 10%). Each row opens the calculator with that setting. What the GGUF names mean.
| Precision | Weights | Total | Smallest setup |
|---|---|---|---|
| As published (BF16) | 58.8 GB | 65.3 GB | A100 / H100 80GB |
| FP16 / BF16 | 58.8 GB | 65.3 GB | A100 / H100 80GB |
| FP8 / INT8 | 29.4 GB | 32.9 GB | A100 40GB |
| INT4 (AWQ / GPTQ) | 15.6 GB | 17.7 GB | RTX 3090 / 4090 |
| GGUF Q8_0 | 31.2 GB | 34.9 GB | A100 40GB |
| GGUF Q6_K | 24.1 GB | 27.1 GB | RTX 5090 |
| GGUF Q5_K_M | 20.8 GB | 23.5 GB | RTX 3090 / 4090 |
| GGUF Q4_K_M | 17.8 GB | 20.1 GB | RTX 3090 / 4090 |
| GGUF Q3_K_M | 14.4 GB | 16.4 GB | RTX 3090 / 4090 |
| GGUF Q2_K | 12.3 GB | 14.1 GB | RTX 4060 Ti 16GB |
KV cache at long context
6 of its 52 layers use full attention and 46 are Mamba or feed-forward layers with no growing cache. Each extra token of context adds 6 KB of FP16 cache per request. How the KV cache works.
| Context | KV cache, FP16 | KV cache, FP8 | Total at Q4_K_M |
|---|---|---|---|
| 4K tokens | 24 MB | 12 MB | 20.1 GB |
| 32K tokens | 192 MB | 96 MB | 20.3 GB |
| 128K tokens | 768 MB | 384 MB | 20.9 GB |
| 256K tokens | 1.50 GB | 768 MB | 21.7 GB |
Which GPUs can run Nemotron 3 Nano 30B-A3B
With 8,192 tokens of context. Several GPUs means one tensor-parallel group of 2, 4 or 8 cards.
| GPU | Memory | Q4_K_M | FP8 |
|---|---|---|---|
| RTX 3060 | 12 GB | Needs 2 | Needs 4 |
| RTX 4060 Ti 16GB | 16 GB | Needs 2 | Needs 4 |
| RTX 3090 / 4090 | 24 GB | Fits on one | Needs 2 |
| RTX 5090 | 32 GB | Fits on one | Needs 2 |
| A100 40GB | 40 GB | Fits on one | Fits on one |
| Mac, 64 GB unified memory about 75% of it is usable by the GPU by default | 48 GB | Fits on one | Fits on one |
| L40S / RTX 6000 Ada | 48 GB | Fits on one | Fits on one |
| A100 / H100 80GB | 80 GB | Fits on one | Fits on one |
| Mac, 128 GB unified memory about 75% of it is usable by the GPU by default | 96 GB | Fits on one | Fits on one |
| H200 | 141 GB | Fits on one | Fits on one |
| B200 | 180 GB | Fits on one | Fits on one |
How fast Nemotron 3 Nano 30B-A3B writes
Tokens per second for one request with 8,192 tokens of context, estimated from memory bandwidth and the parameters read per token. A dash means it does not fit on one card. Try other settings in the speed calculator.
| Hardware | Bandwidth | Q4_K_M | FP8 |
|---|---|---|---|
| RTX 3060 12GB | 360 GB/s | — | — |
| RTX 4090 | 1,008 GB/s | 115–208 | — |
| RTX 5090 | 1,792 GB/s | 181–343 | — |
| M4 Max Mac (128 GB) | 546 GB/s | 68–118 | 43–74 |
| M3 Ultra Mac Studio (512 GB) | 819 GB/s | 97–173 | 63–109 |
| H100 SXM | 3,350 GB/s | 273–557 | 199–382 |
| H200 | 4,800 GB/s | 333–713 | 252–505 |
Longest context on one GPU
How many tokens of context fit on a single card with one request and an FP16 KV cache. "Full" means the model's whole context window fits.
| GPU | Memory | Q4_K_M | Q8_0 | FP8 |
|---|---|---|---|---|
| RTX 3060 | 12 GB | No | No | No |
| RTX 4060 Ti 16GB | 16 GB | No | No | No |
| RTX 3090 / 4090 | 24 GB | 256K (full) | No | No |
| RTX 5090 | 32 GB | 256K (full) | No | No |
| A100 40GB | 40 GB | 256K (full) | 256K (full) | 256K (full) |
| Mac, 64 GB unified memory | 48 GB | 256K (full) | 256K (full) | 256K (full) |
| L40S / RTX 6000 Ada | 48 GB | 256K (full) | 256K (full) | 256K (full) |
| A100 / H100 80GB | 80 GB | 256K (full) | 256K (full) | 256K (full) |
| Mac, 128 GB unified memory | 96 GB | 256K (full) | 256K (full) | 256K (full) |
| H200 | 141 GB | 256K (full) | 256K (full) | 256K (full) |
| B200 | 180 GB | 256K (full) | 256K (full) | 256K (full) |
Model details
- Parameters
- 31.6B (31,577,937,344)
- Experts
- 128 routed experts, all loaded
- Active per token
- 3.5B
- Layers
- 6 of its 52 layers use full attention and 46 are Mamba or feed-forward layers with no growing cache
- Attention cache
- 2 KV heads × 128
- Context length
- 262,144 tokens
- Published weights
- 58.8 GB (BF16)
- On Hugging Face
- nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
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Numbers read from the model files on Hugging Face on .