Model Fine-Tuning & Training
trainingFine-tune language models locally with QLoRA, LoRA, and full fine-tuning. Train custom adapters for domain-specific tasks without sending proprietary data to third-party APIs. VRAM requirements scale with model size and method: QLoRA fine-tuning a 7B model fits in 16GB, while full fine-tuning of 32B models needs 48GB+. System RAM matters: gradient checkpointing and dataset loading use 2-4x the model's VRAM in system memory.
Concurrent VRAM
10 GB
Peak VRAM
16 GB
Min Bandwidth
400 GB/s
Models
3
VRAM Breakdown
How the 10 GB concurrent VRAM is used.
Switched (Loaded As Needed)
These share VRAM with the largest concurrent model. Only one runs at a time.
Q4_K_M
Q4_K_M
Q4_K_M
Local vs API Costs
Typical Monthly API Cost
$200/mo
Break-Even Point
10 months
Annual Savings
~$1920/yr
Based on OpenAI fine-tuning API pricing ($8/1M training tokens, 3-5 fine-tuning runs/month on 50K-row datasets). Local fine-tuning is unlimited iterations with zero per-token cost. Electricity cost ~$15/mo at 6hr/day GPU usage during training. Mid-Range Workstation at ~$1,400. Privacy advantage is the real differentiator: proprietary data never leaves your machine.
Recommended Builds
Pre-configured builds that can run the Model Fine-Tuning & Training workflow.
Mid-Range AI Workstation
The sweet spot for AI: handles most models without overspending
Runs 8 models
AI Builder Workstation
Run every AI tool you need. Nothing leaves your machine
Runs 10 models
High-End AI Workstation
Chat, generate images, and code with AI, all at once
Runs 8 models
Extreme AI Workstation
Dual GPUs that run the biggest AI models at a smart price
Runs 8 models
Prefer a Mac? Apple Silicon with unified memory can run this workflow too. See the Mac AI Builder workflow →