DeepSeek-R1-0528-Qwen3-8B vs NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
Compare benchmark score, parameter size, model family, and practical tradeoffs between these two Hugging Face LLM models.
nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
Metric Comparison
The table keeps the core specs visible for quick evaluation.
| Metric | DeepSeek-R1-0528-Qwen3-8B | NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 | Difference |
|---|---|---|---|
| Benchmark average score | 98.50 | 98.50 | Equal |
| Parameter size | 8.00B | 30.00B | -22B (-275%) |
| Model family | Qwen | Other | Different |
Performance Verdict
Based on the available leaderboard data, deepseek-ai/DeepSeek-R1-0528-Qwen3-8B has the stronger overall benchmark score.
- deepseek-ai/DeepSeek-R1-0528-Qwen3-8B is the stronger performer, scoring 98.50 on average compared to nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16's 98.50.
- nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 is 275% larger in parameter capacity than deepseek-ai/DeepSeek-R1-0528-Qwen3-8B (30.00B vs 8.00B parameters).
- deepseek-ai/DeepSeek-R1-0528-Qwen3-8B is also smaller, which makes its score advantage especially efficient.
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("deepseek-ai/DeepSeek-R1-0528-Qwen3-8B")
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-0528-Qwen3-8B")
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16")
model = AutoModelForCausalLM.from_pretrained("nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16")
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