ToolSite Nemotron 3 Super 120B-A12B VRAM requirements
Nemotron 3 Super 120B-A12B has 123.6B parameters, of which about 12B are used per token; all 512 experts still have to be in memory. With an 8K-token context and one request it needs about 77.2 GB of GPU memory at Q4_K_M, 127 GB at FP8 and 254 GB at FP16/BF16. The published weights take 230 GB (BF16). It does not fit on a 24 GB card at Q4_K_M; the smallest single GPU that holds it with an 8K context is the 80 GB A100 / H100 80GB.
Open Nemotron 3 Super 120B-A12B 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) | 230 GB | 254 GB | 2 × H200 |
| FP16 / BF16 | 230 GB | 254 GB | 2 × H200 |
| FP8 / INT8 | 115 GB | 127 GB | H200 |
| INT4 (AWQ / GPTQ) | 61.2 GB | 67.8 GB | A100 / H100 80GB |
| GGUF Q8_0 | 122 GB | 135 GB | H200 |
| GGUF Q6_K | 94.4 GB | 104 GB | H200 |
| GGUF Q5_K_M | 81.6 GB | 90.3 GB | H200 |
| GGUF Q4_K_M | 69.6 GB | 77.2 GB | A100 / H100 80GB |
| GGUF Q3_K_M | 56.3 GB | 62.5 GB | A100 / H100 80GB |
| GGUF Q2_K | 48.2 GB | 53.6 GB | A100 / H100 80GB |
KV cache at long context
8 of its 88 layers use full attention and 80 are Mamba or feed-forward layers with no growing cache. Each extra token of context adds 8 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 | 32 MB | 16 MB | 77.1 GB |
| 32K tokens | 256 MB | 128 MB | 77.4 GB |
| 128K tokens | 1.00 GB | 512 MB | 78.2 GB |
| 256K tokens | 2.00 GB | 1.00 GB | 79.3 GB |
Which GPUs can run Nemotron 3 Super 120B-A12B
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 8 | Needs more than 8 |
| RTX 4060 Ti 16GB | 16 GB | Needs 8 | Needs more than 8 |
| RTX 3090 / 4090 | 24 GB | Needs 4 | Needs 8 |
| RTX 5090 | 32 GB | Needs 4 | Needs 8 |
| A100 40GB | 40 GB | Needs 2 | Needs 4 |
| Mac, 64 GB unified memory about 75% of it is usable by the GPU by default | 48 GB | Needs 2 | Needs 4 |
| L40S / RTX 6000 Ada | 48 GB | Needs 2 | Needs 4 |
| A100 / H100 80GB | 80 GB | Fits on one | Needs 2 |
| Mac, 128 GB unified memory about 75% of it is usable by the GPU by default | 96 GB | Fits on one | Needs 2 |
| H200 | 141 GB | Fits on one | Fits on one |
| B200 | 180 GB | Fits on one | Fits on one |
How fast Nemotron 3 Super 120B-A12B 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 | — | — |
| RTX 5090 | 1,792 GB/s | — | — |
| M4 Max Mac (128 GB) | 546 GB/s | 22–37 | — |
| M3 Ultra Mac Studio (512 GB) | 819 GB/s | 32–54 | 20–33 |
| H100 SXM | 3,350 GB/s | 114–205 | — |
| H200 | 4,800 GB/s | 152–281 | 101–181 |
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 | No | No | No |
| RTX 5090 | 32 GB | No | No | No |
| A100 40GB | 40 GB | No | No | No |
| Mac, 64 GB unified memory | 48 GB | No | No | No |
| L40S / RTX 6000 Ada | 48 GB | No | No | No |
| A100 / H100 80GB | 80 GB | 256K (full) | No | No |
| Mac, 128 GB unified memory | 96 GB | 256K (full) | No | No |
| H200 | 141 GB | 256K (full) | 256K (full) | 256K (full) |
| B200 | 180 GB | 256K (full) | 256K (full) | 256K (full) |
Model details
- Parameters
- 123.6B (123,611,012,096)
- Experts
- 512 routed experts, all loaded
- Active per token
- 12B
- Layers
- 8 of its 88 layers use full attention and 80 are Mamba or feed-forward layers with no growing cache
- Attention cache
- 2 KV heads × 128
- Context length
- 262,144 tokens
- Published weights
- 230 GB (BF16)
- On Hugging Face
- nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16
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Numbers read from the model files on Hugging Face on .