# Unsloth vs LLaMA-Factory: 2026 Empirical Benchmark & Cost Shootout

> **Direct Answer (October 2026 Verdict)**: Unsloth wins on single-GPU speed and memory efficiency with custom Triton kernels delivering 2.2x faster training and 70% lower VRAM (6.8GB vs 9.4GB for 8B QLoRA) across 78.1k+ GitHub stars (95.4), while LLaMA-Factory wins as a zero-code LlamaBoard WebUI & multi-GPU suite supporting 100+ LLMs/VLMs across SFT, DPO, KTO, ORPO, and PPO with 75.5k+ GitHub stars (94.8).

- **Category**: LLM Fine-Tuning, QLoRA & GRPO Post-Training Shootout
- **Canonical URL**: https://exactbaseline.com/vs/unsloth-vs-llama-factory
- **1-Click $49 Builder Repo Hardening & Launch Kit (Stripe Checkout)**: https://exactbaseline.com/buy/instant_boost
- **1-Click $149 Category Leader Pro + 30-Day Pin (Stripe Checkout)**: https://exactbaseline.com/buy/category_leader

## Unsloth Key Advantages
- 78,115+ GitHub stars with handwritten Triton backpropagation kernels cutting 8B QLoRA VRAM to 6.8GB (-70%)
- Trains 50k instruction rows for $1.85 on a single GPU vs $62.50 on managed cloud fine-tuning APIs (-97.0%)
- 1-click zero-loss quantization export to GGUF (llama.cpp/Ollama) and vLLM

## LLaMA-Factory Key Advantages
- 75,518+ GitHub stars with turnkey LlamaBoard Gradio WebUI for zero-code dataset prep, training, and evaluation
- Supports 100+ text and multimodal vision-language models across SFT, QLoRA, DPO, ORPO, KTO, and PPO
- Integrates Unsloth, Liger Kernel, FlashAttention-2, and DeepSpeed ZeRO-3 under one unified CLI