Blog

Why physical DDR3 and DDR4 RAM cannot mix on one motherboard, but collaborate seamlessly as a pooled AI cluster over local network.

Learn the security risks of raw llama.cpp RPC, why unauthenticated TCP port 50052 is vulnerable, and how to harden your local AI cluster.

Learn how to combine Apple Silicon Macs (Metal) and Windows PCs (CUDA/CPU) into one unified local AI memory cluster over LAN.

Calculate exact memory requirements for Llama 3 70B across Q4, Q8, and FP16 quantizations, context windows, and multi-PC RAM pooling setups.

Learn how to pool CPU RAM across two ordinary PCs over Ethernet to run 32B and quantized 70B AI models without dedicated graphics cards.

Learn how dynamic waterfill layer sharding combines GPU VRAM and motherboard system RAM across computers into one high-speed AI engine.

Deploy private on-premise AI for legal, healthcare, and accounting practices. Zero cloud risk, full ABA/HIPAA compliance, and pooled office hardware.

Stop paying per-seat monthly cloud AI subscriptions. Build a private, on-premise AI server for your business using hardware you already own.

Learn how to pool memory across 5-6 older computers to run a 120-billion parameter LLM locally over LAN without renting data-center GPUs.

Complete command-line walkthrough to build llama.cpp with -DGGML_RPC=ON, launch worker rpc-servers, and configure multi-machine inference over LAN.