Originally published on AI Tech Connect.
What you need to know before you start QLoRA put fine-tuning on consumer hardware. Quantise the base model to 4-bit, train small adapters in higher precision, and a 7B-8B model fits inside a 12 GB GPU you can buy second-hand. It is genuinely cheap. Per 2026 community benchmarks, a full Llama 3 8B fine-tune on ~50,000 examples costs roughly USD 12 of rented compute. A focused 1,000-example run costs a few cents. Data quality beats data volume. Around 1,000 hand-checked examples routinely outperform 100,000 noisy ones. Curation is the real work. Sometimes you should not fine-tune. If you need fresh facts, reach for retrieval-augmented generation first. Fine-tuning changes behaviour, not knowledge. For most of the past few years, "fine-tune your own model" meant a cluster of GPUs, a six-…
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