How Unified Memory Lets Mini PCs Run 70B AI Models That Discrete GPUs Cannot

A technical explainer published on VettedConsumer breaks down why mini PCs with unified memory architectures can run large 70-billion-parameter AI models that traditional discrete GPUs struggle to handle. In conventional setups, a dedicated GPU is limited by its own VRAM, which is separate from the system's main RAM, creating a hard ceiling on model size. Unified memory systems, such as those found in Apple Silicon and certain mini PCs, allow the CPU and GPU to share a single pool of high-bandwidth memory, enabling much larger models to fit in memory. However, the article also notes that while unified memory removes the capacity bottleneck, these systems typically fall behind high-end discrete GPUs in raw inference speed. The piece aims to help consumers understand the trade-offs between memory capacity and processing throughput when choosing hardware for local AI workloads.