Fine-Tuning Llama 3 8B on a Single 12GB RTX 3060 Using Unsloth & QLoRA

Fine-tuning an 8-billion parameter model used to require an 80GB NVIDIA A100 GPU costing $2.50/hour. Today, using Unsloth and custom Triton GPU kernels, you can fine-tune Llama-3.1-8B on an entry-level 12GB RTX 3060 in less than 3 hours with zero out-of-memory errors.

1. Why Unsloth Slashes Training VRAM by 80%

Standard PyTorch autograd saves massive intermediate activation matrices during the forward pass to compute gradients during backpropagation. Unsloth rewrites the backward pass directly in OpenAI Triton, recomputing attention activations on the fly and eliminating up to 80% of activation memory.

3. Complete Python Training Script

from unsloth import FastLanguageModel
import torch

max_seq_length = 2048
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/Meta-Llama-3.1-8B-Instruct",
    max_seq_length=max_seq_length,
    load_in_4bit=True
)

# Attach LoRA adapters with rank 16
model = FastLanguageModel.get_peft_model(
    model,
    r=16,
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
    lora_alpha=16,
    lora_dropout=0
)

print("Peak VRAM during init:", torch.cuda.max_memory_allocated() / 1e9, "GB")