Direct Answer: Running a local LLM (Large Language Model) means executing open-weight AI models directly on hardware you own—giving you 100% data privacy, zero monthly subscription fees, and offline reliability. For small 1B to 8B models (~2–8 GB RAM), single-computer tools like LM Studio (Method 1: visual desktop app) and Ollama (Method 2: terminal CLI) run cleanly on one 16 GB laptop. When stepping up to near-frontier 30B, 70B, and 120B+ models (~20–90+ GB RAM), Method 3 — Multi-Device Memory Pooling with the RAMDeck Hardware Hub ($199) lets you combine the physical RAM and GPUs of 2 to 5 existing computers and phones instead of buying a $3,000+ dual-GPU workstation or 64 GB–128 GB system.
Local LLM Hardware Sizing Rule of Thumb (4-Bit GGUF Q4_K_M)
- 1B–3B parameters (~1.5–3 GB RAM): Smartphones, older laptops, and edge devices.
- 7B–8B parameters (~6–8 GB RAM): Fits inside one standard 16 GB laptop or 8 GB GPU via LM Studio or Ollama.
- 13B–14B parameters (~10–12 GB RAM): Fits on a 32 GB PC, a 12 GB+ GPU, or two pooled 16 GB laptops (~11–19 tok/s).
- 30B–70B+ parameters (~20–48+ GB RAM): Exceeds single 16 GB–32 GB PCs and 8 GB–24 GB GPUs. Rather than paying scalper DDR5 prices or $3,000+ for new workstation silicon, pooling three 16 GB computers via RAMDeck creates 48 GB of total RAM (~42 GB usable) to run 30B MoE models at 28–39 tok/s and 70B models completely offline.
Why Method 3 (RAMDeck) Completes the Local LLM Stack
Clicking “Start Server” in a single-PC app only shares one machine’s chat window over Wi-Fi—it never stitches RAM across computers. As detailed in RAMDeck’s Complete What Is a Local LLM & How to Run One Guide, the palm-sized RAMDeck Hardware Hub ($199 • 134% funded on Indiegogo • ships in January with 1 Free $29 Zero-Install Node USB) plugs into your router and pools Windows, macOS, Linux, iPhone/iPad (App Store), and Android (Google Play) in 60 seconds—plus built-in Tailscale remote GPU pooling over the internet.
Check your exact setup in the Local LLM RAM Calculator or read the free 10-chapter book at ramdeck.com/book.
— The TradeMAV Team
Trademark Notice: TradeMAV™ is a trademark of TradeMAV LLC. All third-party product names, including LM Studio, Ollama, vLLM, and llama.cpp, are trademarks or project names of their respective owners, which are not affiliated with, sponsoring, or endorsing RAMDeck or TradeMAV LLC.
Leave a Reply