Fine-tuning 70B+ models on custom datasets is now feasible without datacenter infrastructure. The barrier to entry has dropped significantly - teams can leverage distributed compute networks, consumer-grade GPU clusters, or cloud spot instances with proper orchestration. Key enablers: QLoRA/LoRA for parameter-efficient fine-tuning (reducing VRAM requirements by 4-10x), gradient checkpointing, and DeepSpeed ZeRO optimizations. A typical setup: 4-8x A100s or even RTX 4090 clusters can handle 70B fine-tuning jobs in reasonable timeframes. The real unlock is data quality over quantity - 1000 high-quality examples often outperform 100k mediocre ones. Most teams waste resources on infrastructure when the bottleneck is actually curating domain-specific training data that captures their unique use case. The compute cost for a full 70B fine-tune has dropped from $50k+ to under $5k with the right setup.