Nvidia’s Llama 3.1 Neatron Ultra 253B: The New AI Model Redefining Performance

Introduction:

In a surprising and strategic move, Nvidia has unveiled its latest open-source large language model – Llama 3.1 Neatron Ultra 253B. Despite being based on Meta’s older Llama 3.145B model, Nvidia’s version has surpassed expectations, outperforming even newer, larger competitors in major AI benchmarks. Here’s everything you need to know about this groundbreaking model and why it could change the AI game.

1. What Is Nvidia’s Neatron Ultra 253B?

Nvidia’s new open-source AI model is called Llama 3.1 Neatron Ultra 253B. It packs 253 billion parameters and supports dual behavior modes: a high-reasoning mode for complex tasks and a casual mode for lightweight responses. This flexibility makes it ideal for a wide range of applications, from chatbot conversations to high-stakes research.

2. Built on Meta’s Llama, But Better

Even though Nvidia used Meta’s older Llama 3.145B Instruct model as a foundation, their new version has:

• Surpassed newer models like DeepSeek R1 on key performance tests
• Been fully released on Hugging Face, including weights, code, and post-training data
• Opened up possibilities for developers around the world to build on it

3. Core Innovations That Power the Model

Nvidia used neural architecture search to create a smarter structure that:

• Skips attention layers when not needed to save memory
• Blends feed-forward networks more efficiently
• Compresses and optimizes operations for speed and performance

With just 8 H100 GPUs, the entire model can be run on a single machine – a feat unheard of at this scale.

4. How Nvidia Trained and Fine-Tuned the Model

Post-training steps included:

• Supervised Learning: For tasks like math, coding, tool use, and conversation
• Reinforcement Learning: Using group relative policy optimization to improve instruction following
• Knowledge Distillation: Ingesting 65B+ words and then an additional 88B to embed expert-level understanding
• Dataset Sources: Included FineWeb, BuzzV 1.2, and Dolma, among others

This careful process helped the model become not just smart, but also reliable and context-aware.

5. Head-to-Head: Nvidia Neatron vs DeepSeek R1

Despite DeepSeek R1 having 671 billion parameters, Nvidia’s smaller model:

• Scored 76.1% on GPQA (vs DeepSeek’s 56.6%)
• Jumped from 29.3% to 66.3% on Live Code Bench when reasoning was activated
• Outperformed on EvilEval and tool-based tasks
• Held its own on math benchmarks like MATH500 and AME25

Nvidia ran up to 16 trials per evaluation with 32,000-token inputs to ensure accuracy.

6. Why This Model Matters for AI Developers

• Fully Open Source: From model weights to training data
• Lightweight and Fast: Works even on a single 8-GPU setup
• Dual Modes: Lets you switch between deep reasoning and fast replies
• Hardware Flexibility: Works on H100, B100, and Hopper architecture
• Great for Tool Use: Excels at multi-step problem solving and code generation

Whether you’re running a data center or experimenting in a lab, this model offers high performance without bloated infrastructure needs.

7. Final Thoughts

Nvidia’s Llama 3.1 Neatron Ultra 253B model is more than just a large language model – it’s a signal of where the future of AI is headed: open, powerful, flexible, and accessible. With a smaller footprint and better real-world utility than many of its larger competitors, this release could reshape how developers think about building and deploying advanced AI systems.

8. FAQs

Q: What makes Nvidia’s Neatron model different?

A: It’s leaner, runs faster on less hardware, and offers both reasoning and casual response modes.

Q: Can I access and build on the model?

A: Yes. The model, code, weights, and post-training datasets are freely available on Hugging Face.

Q: How does it perform in math and coding tasks?

A: It excels with up to 97% accuracy on Math500 and 66% on Live Code Bench when reasoning mode is enabled.

Q: Is this model better than DeepSeek R1?

A: In many tasks, yes. Despite being smaller, it matches or outperforms DeepSeek R1 on multiple benchmarks.

Q: What hardware do I need to run it?

A: An 8x H100 GPU setup is enough. It also works with B100 and Hopper chip architecture.