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Side-by-side model comparison

NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 vs TinyLlama-1.1B-Chat-v1.0

Compare benchmark score, parameter size, model family, and practical tradeoffs between these two Hugging Face LLM models.

Model A

nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4

Benchmark score 98.50
Parameters 120.00B
Model family Other
Dataset status Available
Model B

TinyLlama/TinyLlama-1.1B-Chat-v1.0

Benchmark score 98.50
Parameters 1.10B
Model family Llama
Dataset status Available

Metric Comparison

The table keeps the core specs visible for quick evaluation.

Live dataset
Metric NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 TinyLlama-1.1B-Chat-v1.0 Difference
Benchmark average score 98.50 98.50 Equal
Parameter size 120.00B 1.10B +118.9B (+10809.1%)
Model family Other Llama Different

Performance Verdict

Based on the available leaderboard data, nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 has the stronger overall benchmark score.

  • nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 is the stronger performer, scoring 98.50 on average compared to TinyLlama/TinyLlama-1.1B-Chat-v1.0's 98.50.
  • nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 is 10809.1% larger in parameter capacity than TinyLlama/TinyLlama-1.1B-Chat-v1.0 (120.00B vs 1.10B parameters).
  • nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 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.

Python tutorial
Load Model A (NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4) 
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")
Load Model B (TinyLlama-1.1B-Chat-v1.0) 
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")

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