Can I run NVIDIA Nemotron 3 Super 120B A12B (MoE) locally?
NVIDIA Nemotron 3 Super 120B A12B (MoE) has 123.61 billion parameters (Hugging Face). At the popular Q4_K_M quantization it needs roughly 79.3 GB of GPU memory ESTIMATE. It has been on Hugging Face since 2026-03-10. It's a mixture-of-experts model: all weights must fit in memory, but it generates faster than a dense model of the same size.
Memory needed by quantization
| Quant | Weights | Total needed (est.) |
|---|---|---|
| IQ1_S | 24.1 GB | 25.9 GB |
| IQ2_XXS | 31.8 GB | 34.1 GB |
| IQ2_M | 41.7 GB | 44.4 GB |
| Q2_K | 49.0 GB | 52.1 GB |
| IQ3_XXS | 47.3 GB | 50.3 GB |
| Q3_K_S | 56.4 GB | 59.9 GB |
| IQ3_M | 56.6 GB | 60.0 GB |
| Q3_K_M | 61.8 GB | 65.5 GB |
| IQ4_XS | 65.7 GB | 69.6 GB |
| Q4_0 | 69.5 GB | 73.6 GB |
| Q4_K_M | 74.9 GB | 79.3 GB |
| MXFP4 | 71.5 GB | 75.8 GB |
| Q5_K_M | 87.9 GB | 92.9 GB |
| Q6_K | 101.4 GB | 107.1 GB |
| Q8_0 | 131.3 GB | 138.5 GB |
| F16 | 247.2 GB | 260.2 GB |
KV cache by context length ESTIMATE
| Context | f16 | q8_0 | q4_0 |
|---|---|---|---|
| 4k | 0.03 GB | 0.02 GB | 0.01 GB |
| 8k | 0.07 GB | 0.04 GB | 0.02 GB |
| 16k | 0.13 GB | 0.07 GB | 0.04 GB |
| 32k | 0.27 GB | 0.14 GB | 0.08 GB |
| 64k | 0.54 GB | 0.29 GB | 0.15 GB |
| 128k | 1.07 GB | 0.57 GB | 0.30 GB |
From config.json: 8 KV-caching attention layers, 2 KV heads × 128 head dim. Max context 256k. The tables above assume 4k context.
Which GPUs and Macs can run it?
✅ fits · ⚠️ fits with little headroom · ❌ doesn't fit on this one device (multiple GPUs or RAM offload may work; check your setup). Apple Silicon assumes macOS lets the GPU use ~67–75% of unified memory. All fit verdicts are estimates.
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