Hey r/SideProject, If you ask any PC forum if you can pool RAM across old computers, the answer is always an emphatic "No." And for gaming or operating systems, they are right- memory bus speeds cannot cross motherboards over Ethernet. However, for local LLMs, transformer layers process sequentially. Weights only load once into RAM at startup, and during token generation, only the small intermediate activation vector (~650 KB/token) crosses the network. To solve AI memory walls without buying expensive enterprise GPUs, we built RAMDeck. Watch our live cluster test running a 120B model across 6 mismatched devices: https://www.youtube.com/watch?v=ASiUyIuVkGc What it does: Zero-Config Discovery: Discovers mixed machines on your LAN (Windows, Mac, Android) via mDNS. Linux coming soon! Unified RAM Pool: Partitions the model's neural network layers across each computer's physical memory. Dynamic Waterfilling: Prioritizes fast GPUs first, then local host RAM, then remote network nodes. Self-Healing: If a node drops or a laptop lid closes, it automatically redistributes the remaining shards so inference continues without crashing. OpenAI-Compatible API: Exposes localhost:8420/v1/chat/completions for direct use with Open-WebUI, Cursor or LibreChat. Live Proof: We tested this by pooling 78 GB of RAM across 6 disparate devices (a mini PC with an RTX 3060, an old i5 laptop, 2 Macbooks (M3 and 2017 Intel), mac mini M4 and an Android phone) to run a 60 GB 120B model (command-r-plus-104b-q4) at 1.37 tok/s for a 3-day continuous stress test. Project & Licensing: The core engine is source-available on GitHub. For people who want a plug-and-play appliance rather than configuring headless boxes, we are also offering turnkey hardware hubs on Indiegogo. GitHub (Source-Available Core): https://github.com/trademav/ramdeck-core-public Speed Benchmarks & Hardware Breakdown: https://ramdeck.com/how-to-pool-ram-from-old-pcs/ Hardware Hub on Indiegogo: https://www.indiegogo.com/en/projects/trademav/ramdeck-pool-your-ram-run-bigger-ai Happy to answer any questions about the network latency bottlenecks, llama.cpp RPC performance or cross-platform memory constraints!   submitted by   /u/Medicine_Blogscanner [link]   [comments]