A full fine-tune of Llama 3.1 70B on 50,000 training samples costs approximately $2,400 using on-demand RTX 6000 Pro cloud instances — but the same job on a dedicated RTX 6000 Pro with QLoRA drops to under $85. The method you choose and the GPU you run it on determine whether fine-tuning is a budget line item or a budget breaker.
Fine-Tuning Methods Compared
Three approaches dominate LLM fine-tuning today. Full fine-tuning updates every parameter and requires the most VRAM — a 70B model needs 4x RTX 6000 Pro 96 GB GPUs minimum. LoRA (Low-Rank Adaptation) trains small adapter layers, cutting VRAM by 60-70%. QLoRA combines 4-bit quantisation with LoRA, enabling 70B fine-tuning on a single RTX 6000 Pro 96 GB. Each method trades off training time, VRAM, and final model quality differently.
Fine-Tuning Cost by GPU and Method
| Model Size | Method | GPU Required | Training Time (50K samples) | Dedicated GPU Cost | Cloud On-Demand Cost |
|---|---|---|---|---|---|
| 7B | Full Fine-Tune | 1x RTX 6000 Pro 96 GB | 4 hours | $12 | $16 |
| 7B | LoRA | 1x RTX 5090 | 3 hours | $5 | $9 |
| 7B | QLoRA | 1x RTX 5090 | 3.5 hours | $6 | $10 |
| 13B | LoRA | 1x RTX 6000 Pro 96 GB | 6 hours | $18 | $24 |
| 13B | QLoRA | 1x RTX 5090 | 8 hours | $14 | $24 |
| 70B | Full Fine-Tune | 4x RTX 6000 Pro 96 GB | 18 hours | $216 | $576 |
| 70B | LoRA | 2x RTX 6000 Pro 96 GB | 12 hours | $72 | $192 |
| 70B | QLoRA | 1x RTX 6000 Pro 96 GB | 14 hours | $42 | $56 |
Dedicated GPU costs based on GigaGPU monthly rates amortised per hour. Cloud on-demand reflects typical UK/EU spot pricing.
Break-Even: Dedicated vs Cloud for Fine-Tuning
If you fine-tune once, cloud on-demand makes sense. But most teams iterate — running 5-15 training experiments before settling on a production model. At three fine-tuning runs per month on a 70B model with LoRA, a dedicated GPU server pays for itself within the first month compared to on-demand cloud. The TCO analysis of dedicated vs cloud shows dedicated hosting breaks even at just 40% monthly utilisation for training workloads.
VRAM Requirements Drive GPU Selection
VRAM is the hard constraint. Full fine-tuning loads the model, optimiser states, and gradients simultaneously. A 7B model at FP16 needs roughly 28GB for full fine-tuning (model weights plus AdamW states). QLoRA compresses the base model to 4-bit and only trains adapter weights in FP16, reducing peak VRAM from 28GB to under 10GB for a 7B model. This is why QLoRA on an affordable GPU like the RTX 5090 (24GB VRAM) is the most cost-effective path for most teams.
For 70B full fine-tuning, you need multi-GPU clusters with NVLink or InfiniBand interconnects. The communication overhead between GPUs adds 15-25% to total training time, which factors into cost.
Hidden Costs in Fine-Tuning Pipelines
GPU hours are only part of the bill. Data preparation — cleaning, formatting, and validating training datasets — often takes 2-5x the compute time in engineering hours. Evaluation runs after each experiment consume additional GPU time. Storage for checkpoints adds up quickly: a single 70B checkpoint is approximately 140GB. Factor in 5-10 checkpoints per run and storage costs become meaningful. Our LLM cost calculator includes these ancillary costs in its estimates.
Start Fine-Tuning on Dedicated GPUs
Fine-tuning on GigaGPU dedicated servers gives you persistent access to high-VRAM GPUs without hourly billing anxiety. Run as many experiments as your schedule demands at a flat monthly rate. Deploy your fine-tuned model immediately to the same server for production inference with vLLM.
Explore open-source LLM hosting options or compare training costs with our GPU vs API cost comparison. For enterprise training at scale, private AI hosting provides isolated environments with guaranteed resources. More cost breakdowns on the cost blog.
