How to Fine-Tune Llama 3.3 with LoRA & Unsloth on a Single GPU (2026 Guide)

Fine-tuning custom open-weights models allows enterprises and indie developers to train LLMs on proprietary domain data without leaking secrets to third-party APIs. Using Unsloth in 2026, you can fine-tune Llama 3.3 8B on a single 16GB GPU in under 45 minutes.

Why Unsloth for LoRA in 2026?

• 2x Faster Training: Hand-written OpenAI Triton GPU kernels accelerate backpropagation.
• 70% Less Memory: Enables fine-tuning 70B parameter models using 4-bit QLoRA on consumer hardware.
• Zero Accuracy Loss: Retains 100% mathematical precision identical to standard HuggingFace PyTorch trainers.

from unsloth import FastLanguageModel
import torch

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Llama-3.3-8B-Instruct",
    max_seq_length = 4096,
    load_in_4bit = True,
)

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,
    bias = "none",
)