Hardware Recommendations for LLM Fine-Tuning: The 2026 Guide

The landscape of LLM fine-tuning has shifted from 'can we run it' to 'how fast can we converge.' As models like Llama 4 push parameter counts and context windows further, the hardware bottleneck has moved beyond raw TFLOPS to VRAM capacity and interconnect bandwidth. Engineers often underestimate the memory overhead of optimizer states and gradients, leading to frequent OOM errors on consumer-grade or poorly orchestrated cloud hardware. At Lyceum Technology, we see teams losing weeks to infrastructure debugging that could be solved by matching the right silicon to the specific fine-tuning method. This article provides a technical deep dive into the hardware requirements for full fine-tuning, LoRA, and quantized workflows in the current 2026 ecosystem.