1. ~/home
  2. ~/blog
  3. ./fine-tuning-llama3-qlora.md

Fine-Tuning Llama 3 on a Single GPU with QLoRA

  • author: "Vikas Dhyani"
  • date: "2026-03-27"
  • read_time: "~1 min"

Fine-tuning large models once required complex GPU clusters. With **QLoRA** (Quantized Low-Rank Adaptation), we freeze base model weights in 4-bit precision, adding small trainable adapter layers. This allows fine-tuning Llama 3 on consumer GPUs.


# Loading model with bitsandbytes quantization configuration
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True
)
model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B", quantization_config=bnb_config)
<- Back to logs

One log a week. No hype.

Join 1,000+ developers getting practical insights on full-stack AI engineering, vectors optimization, and agent security. Direct to your inbox.

  • One practical engineering walkthrough every week
  • Real systems and architectural evaluations
  • No spam, unsubscribe with a single click

Why Work With Me?

17+ Years of
Experience

Proven experience building secure, reliable, and business-critical software systems.

AI + Enterprise
Expertise

Practical AI solutions integrated with scalable enterprise architecture.

End-to-End
Ownership

From requirements and architecture through development, deployment, and support.

Clear
Communication

Transparent progress, realistic timelines, and maintainable solutions.