Academic labs need enough VRAM for honest experiments on mainstream models plus fine-tuning. The RTX 5060 Ti 16GB on our hosting hits that bar at a low fixed cost – perfect for a postgrad group or small research team.
Contents
What Fits
- Eval-scale inference of Llama 3 8B, Qwen 14B, Gemma 9B, Phi-3, Mistral 7B
- QLoRA fine-tuning up to 13-14B
- LoRA fine-tuning up to 8B
- Diffusion research (SDXL, FLUX.1-schnell, smaller custom models)
- Embedding training and inference at scale
- Classifier fine-tuning (BERT/DeBERTa family)
Typical Experiments
- Quantisation ablation – FP16 vs FP8 vs AWQ on a benchmark suite
- Prompt engineering sweeps – MMLU / HellaSwag / HumanEval under various prompt templates
- Fine-tune recipes – compare LoRA ranks, learning rates, datasets
- Inference efficiency – measure tokens/watt or latency under various optimisations
- Small-scale diffusion experiments
Fine-Tune Budget
| Experiment | Time |
|---|---|
| Llama 3 8B QLoRA, 10k samples | ~35 min (Unsloth) |
| Qwen 14B QLoRA, 10k samples | ~60 min (Unsloth) |
| Phi-3 full fine-tune, 5k samples | ~90 min |
| DeBERTa classifier, 50k samples | ~30 min |
Overnight you can sweep dozens of hyperparameter settings.
Cost vs Cloud
- A single 5060 Ti dedicated box on our UK hosting = flat monthly fee
- Equivalent AWS/Azure A10G or T4 for the same uptime: much more
- Lambda / Vast spot pricing: competitive if you plan carefully, but variable
- For labs with steady workload, dedicated hosting wins
See vs Lambda Labs and vs Colab Pro for cost comparisons.
Research Lab on Blackwell 16GB
Flat cost, full root, UK-based. UK dedicated hosting.
Order the RTX 5060 Ti 16GBSee also: development sandbox, fine-tune throughput, Jupyter setup, Unsloth.
