Fine-tune Workstation

Why this build

Fine-tuning has different hardware demands than inference. LoRA training on an 8B model requires holding model weights, gradients, optimizer states, and activations in VRAM simultaneously — roughly 2–3× the memory needed for inference. The RTX 4090's 24 GB is the minimum for LoRA on 14B without gradient checkpointing; with gradient checkpointing you can push to full fine-tuning on 8B. The 64 GB DDR5 matters because training dataloaders and tokenized datasets load into system RAM, and Python's memory footprint during training is substantial. The 7800X3D's cache advantages apply here too: tokenization throughput and dataset preprocessing are CPU-bound steps that run between gradient updates. This build is scoped to LoRA and QLoRA workflows — not full-parameter fine-tuning (FSDP) or training runs above 14B. Those require multi-GPU setups with NVLink, typically on server-class hardware.