Architecture Deep Dive

Inside QLoRA: How 4-Bit Fine-Tuning Fits LLMs on One GPU

QLoRA fine-tunes 65B-parameter LLMs on a single 48GB GPU using NF4 quantization, double quantization, and paged optimizers. Deep-dive on each technique and its production trade-offs.

Published May 11, 2026

16 min read

AI Tools Kit

AI Tools Kit provides free developer tools for working with AI language models. Built by developers, for developers.

Learn more about us →

Architecture Deep Dive

Inside vLLM: How PagedAttention Enables High-Throughput LLM Serving

vLLM's PagedAttention algorithm achieves 24x higher throughput than HuggingFace Transformers by applying OS virtual memory concepts to KV cache management. Here's how the architecture actually works.

Inside QLoRA: How 4-Bit Fine-Tuning Fits LLMs on One GPU

Related Articles

Inside vLLM: How PagedAttention Enables High-Throughput LLM Serving