As agentic models like Nemotron 3 Nano and frameworks like Unsloth bring enterprise-grade AI to the desktop, the most common question is: What hardware do I actually need to run this?
Because fine-tuning is significantly more memory-intensive than simple inference (chatting), your requirements will shift based on your specific goals. Here is a breakdown of the hardware tiers for the modern AI workflow.
1. The Inference Tier (Running & Chatting)
To simply run a model like Nemotron 3 Nano (30B) for daily tasks, you need enough VRAM to hold the model weights and the “context” (the conversation history).
- Optimal (24GB VRAM): An NVIDIA RTX 3090, 4090, or 5090 allows you to run a high-quality 4-bit or 5-bit version of Nemotron 3 Nano entirely on the GPU. This delivers “blazing” speeds of 40+ tokens per second.
- Minimum (12GB – 16GB VRAM): You can run these models on cards like the RTX 4070 Ti or 5070, but you will likely need to “offload” some of the model to your system RAM. This is functional but significantly slower (around 2–5 tokens per second).
2. The Fine-Tuning Tier (Teaching New Tricks)
Fine-tuning requires extra memory for “optimizer states” and “gradients”—the math used to update the model.
| Method | Minimum VRAM (for 7B-8B Models) | Minimum VRAM (for 30B Models) |
| QLoRA (4-bit) | 6GB – 8GB | 20GB – 24GB |
| LoRA (16-bit) | 15GB – 16GB | 64GB+ (Multi-GPU/Workstation) |
| Full Fine-Tuning | 60GB – 80GB | 280GB+ (DGX Spark territory) |
Pro Tip: If you have a single 24GB card (like an RTX 4090), you are in the “sweet spot” for QLoRA fine-tuning on models up to 30B parameters.
3. The Power User Tier: NVIDIA DGX Spark
For those moving beyond 4-bit approximations and into full-scale agent development, consumer hardware hits a ceiling. This is where DGX Spark steps in.
- Unified Memory: Unlike a PC where the GPU and CPU have separate RAM, DGX Spark features 128GB of unified LPDDR5x memory.
- What this unlocks: You can fine-tune 70B parameter models locally or perform Full Fine-Tuning on 8B models that would normally require a server cluster.
- Daisy-Chaining: Two DGX Spark units can be linked via 200Gbps connections to effectively double the memory, allowing for local work on models up to 400B+ parameters.
Software Essentials
Regardless of your hardware, the software stack remains the same:
- OS: Windows 11 (via WSL2 or Docker) or Linux (Ubuntu 22.04+ recommended).
- Drivers: Latest NVIDIA Game Ready or Studio Drivers (CUDA 12.x support required).
- Python: 3.10 to 3.13 are currently the most stable for Unsloth.