LLM Training

Training and fine-tuning need a different kind of GPU planning than serving does: multi-day jobs, checkpoint resilience, and cluster-scale networking instead of single-request latency. This cluster covers the full range, from a $5 LoRA fine-tune on a single GPU up to distributed pretraining runs across dozens of nodes.

On the fine-tuning side you'll find LoRA and its newer variants (DoRA, GaLore, PiSSA, VeRA), full RLHF pipelines with DPO, PPO, and GRPO, and framework comparisons (Axolotl vs Unsloth vs TorchTune) that actually ran the same job on each. On the infrastructure side: FSDP and DeepSpeed ZeRO-3 multi-node setup, NCCL tuning for InfiniBand and Ethernet fabrics, and spot-instance training resilience, because a 60% cost saving is worthless if a preemption wipes out your checkpoint.

Two case studies show what this looks like in practice: a 12-person startup that trained a 70B model for $11,200 on spot GPUs, and a team running 10 concurrent fine-tuning jobs on bare metal at 94% utilization. The pillar post, How to Fine-Tune LLMs in 2026, is the starting point: real costs, GPU requirements, and working code for Llama, Qwen, and DeepSeek. Read it first, then move into whichever technique or scale your job actually needs.