Can you believe it? An AI model with only 9 billion parameters runs 6x faster than Qwen3-8B while saving up to 60% inference cost. NVIDIA has just unveiled the Nemotron Nano 2, the world’s first Hybrid Mamba-Transformer model, combining lightning-fast performance with enterprise-grade reasoning.
And here’s the game-changer: a revolutionary feature called “Thinking Budget”, allowing you to control exactly how much “thinking” the AI does—optimizing for speed, cost, or accuracy depending on your use case.
Nemotron Nano 2: When Mamba Meets Transformer
Hybrid Architecture – The Secret Behind the Speed
The problems with pure Transformers:
- KV-cache grows rapidly on long sequences
- Memory usage scales exponentially
- Slows down significantly on long outputs
- Extremely inefficient on tasks requiring long “thinking traces”
Nemotron Nano 2’s breakthrough solution:
- Mamba-2 layers (majority): Linear complexity, fixed memory per token
- Strategic Attention “Islands”: Preserve global content-based jumps
- Perfect balance: Transformer-level accuracy + Mamba-level efficiency
Inside the Nemotron Nano 2 Architecture
- 62 layers total: 28 Mamba-2 + 6 Attention + 28 FFN
- Model dimension: 5120
- FFN dimension: 20480
- Attention setup: 40 query heads, 8 KV heads (Grouped-Query)
- Mamba config: 8 groups, state dim 128, expansion factor 2
➡️ The result: linear-time performance instead of quadratic, especially effective for long reasoning sequences.
“Thinking Budget” – Revolutionary Cost Control for AI
Nemotron Nano 2 introduces a two-phase process:
- Thinking phase: Internal reasoning within a set budget
- Response phase: Final answer based on distilled insights
Real-world benefits:
- Up to 60% inference cost reduction
- Predictable latency control
- Flexible: adjust budget for different industries and applications
- SLA-friendly: guaranteed response times for production
Smarter Compression: From 12B to 9B With No Accuracy Loss
- Starting point: Nemotron-Nano-12B-v2-Base
- Target: 19.66 GiB memory budget (compatible with A10G)
- Neural Architecture Search (NAS):
- Reduced depth: 62 → 56 layers
- Optimized width via embedding, FFN, and Mamba heads pruning
- Knowledge Distillation:
- Teacher-student transfer from 12B model
- Forward KL divergence loss for accurate knowledge transfer
➡️ Outcome: Nemotron Nano 2 (9B) matches its 12B teacher model in performance.
Why Nemotron Nano 2 Is a Game-Changer
- 6x faster than Qwen3-8B
- 60% cheaper inference cost
- Smarter, not bigger: Hybrid Mamba-Transformer proves future AI is about intelligent architecture, not just scale
- Production-ready: Optimized for enterprise deployments and developers alike
🔥 Stay Tuned for More
In upcoming posts, we’ll cover:
✅ Hands-on tutorial: Deploy Nemotron Nano 2 with vLLM
✅ Performance benchmarks vs LLaMA, Qwen, GPT models
✅ Best practices for Thinking Budget optimization
✅ Advanced production tips for hybrid AI
👉 What do you think about this hybrid AI architecture? Share your thoughts in the comments below!
