Breaking VRAM Limits: Running 70B Models on Consumer GPUs

As local LLM inference becomes essential for cost-conscious developers, VRAM capacity has emerged as the primary bottleneck determining which models remain practically unfeasible. This guide reveals how quantization and strategic GPU layer splitting can unlock 70B-parameter models on 8GB consumer GPUs, transforming what appears impossible into an achievable engineering challenge. For anyone building local AI systems, mastering these VRAM optimization techniques shifts the limiting factor from hardware inadequacy to intelligent resource allocation.