NVIDIA-Nemotron-3-Super-120B-A12B-BF16 vs NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4
Compare benchmark score, parameter size, model family, and practical tradeoffs between these two Hugging Face LLM models.
nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4
Metric Comparison
The table keeps the core specs visible for quick evaluation.
| Metric | NVIDIA-Nemotron-3-Super-120B-A12B-BF16 | NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 | Difference |
|---|---|---|---|
| Benchmark average score | 98.50 | 98.50 | Equal |
| Parameter size | 120.00B | 120.00B | Equal |
| Model family | Other | Other | Match |
Performance Verdict
Based on the available leaderboard data, nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16 has the stronger overall benchmark score.
- nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16 is the stronger performer, scoring 98.50 on average compared to nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4's 98.50.
- Both models share the exact same parameter size of 120.00B parameters.
- nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16 has more parameter capacity, which may contribute to its stronger benchmark score.
Integration & Implementation Guide
Learn how to load and execute these models programmatically in Python using Hugging Face's transformers library.
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16")
model = AutoModelForCausalLM.from_pretrained("nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16")
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4")
model = AutoModelForCausalLM.from_pretrained("nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4")
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