AI Workflow

Model Fine-Tuning & Training

training

Fine-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.

AxolotlUnslothHugging Face TRLLLaMA FactoryPEFT

Concurrent VRAM

10 GB

Peak VRAM

16 GB

Min Bandwidth

400 GB/s

Models

Memory

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.

Llama 3.1 8B Instruct(qlora fine tuning target)

10 GB

Q4_K_M

Mistral 7B Instruct v0.3(qlora fine tuning target)

10 GB

Q4_K_M

Qwen 2.5 Coder 7B Instruct(code fine tuning target)

10 GB

Q4_K_M

Buying Priority

What matters most for this workflow

This workflow fits on surprisingly modest hardware, so the main decision is whether you want the cheapest workable setup or enough headroom to keep the experience snappy.

Practical Tradeoff

How to think about the hardware

This is one of the cleaner local-first cases: if you use the tools regularly, the economics catch up quickly and privacy/offline access become a bonus rather than the only justification.

Return on Investment

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.

Hardware