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Super's NVFP4 training does not make a downloaded BF16 checkpoint a ready-to-run four-bit artifact.
Nemotron 3 Super and Nano are different released models, not just two labels on the same reasoning configuration. A useful comparison distinguishes architecture, training precision and the artifact used for inference.
Identify the exact models
The Super card identifies a 120B-A12B checkpoint with a hybrid LatentMoE design. The Nano card identifies the smaller 30B-A3B family member.
The Super report describes pre-training in NVFP4 and the use of latent expert computation. The Nano report describes its own hybrid design. A training-precision statement does not mean that an artifact labelled BF16 is secretly a ready-to-run four-bit checkpoint.
Understand the latent expert distinction
Super projects representations into a smaller latent dimension for expert routing and computation. This is an architectural mechanism, not simply a file-compression setting applied after training.
Nano and Super also differ in scale and training details. A difference in final task quality cannot be assigned solely to LatentMoE without a controlled ablation. Keep the complete released model identity as the comparison unit.
Choose tasks that reveal an escalation need
Use a fictional constraint-assignment problem with an independently enumerable solution. Add a second task requiring a small code repair whose edge condition is explicitly defined.
| Task outcome | Selection implication to investigate |
|---|---|
| Both accepted | The smaller candidate may be sufficient for this fixture |
| Only one accepted | Preserve the exact failing condition |
| Neither accepted | Inspect task preparation and reference consistency |
| Output incomplete | Examine the declared completion allowance |
| Different valid solutions | Accept both when all requirements hold |
These are interpretation rules, not measurements already performed. The example set should not be presented as a benchmark result until the actual runs exist.
Keep reasoning controls and artifacts explicit
Use each checkpoint’s documented template and reasoning behavior. Match the supplied task facts and reference checks, while preserving any differences in supported control mechanisms.
Record the actual downloaded dtype and quantization metadata. Do not compare a low-bit conversion of one model against a wider artifact of the other without naming that additional condition. Loading compatibility remains separate from accepted task behavior.
Make a bounded escalation decision
Keep revisions, prompts, generation settings, artifacts and final-answer checks together. Record failed and incomplete attempts rather than selecting only impressive successes.
The evidence can justify escalating certain unresolved reasoning or code tasks from Nano to Super, or retaining Nano when it already meets the requirement. It does not establish universal superiority, guaranteed hardware fit or a fixed throughput ratio. The architecture and training report explain the candidates; the checked final answers determine whether the additional model is useful for the specific workload.