Distributed AI on Consumer Hardware: Ollama vs. Exo vs. llama.cpp RPC vs. RAMDeck (The Click-and-Play Way to Pool RAM Across Windows, Mac, Linux, iPhone & Android)
When pooling consumer hardware to run Large Language Models (LLMs) locally, users face three distinct options:
1. Option A — Centralized Sharing (Ollama): Easy to install on one machine, but it does not combine RAM. Your model size is strictly limited to whatever fits inside that single computer.
2. Option B — Developer CLI Frameworks (Exo & llama.cpp RPC): Combines RAM across machines, but requires typing terminal commands, lacks a native Windows desktop installer in Exo, and lacks official Apple App Store or Google Play Store apps.
3. Option C — Click-and-Play Hardware Hub & Apps (RAMDeck): The first plug-and-play router hardware appliance ($199 retail / $159 Indiegogo special + Free $29 Node USB) that actively pools RAM across all 5 operating systems—Windows 10/11, macOS, Linux, iOS (Apple App Store), and Android (Google Play Store)—with a click-and-play web interface, a zero-install “Unplug & Walk” Node USB, and zero terminal commands.
1. Centralized Sharing (Ollama) vs. True Distributed Inference (RAM Pooling)
A common misconception when setting up local AI across multiple computers is confusing Centralized Sharing with True Distributed Inference (Network RAM Pooling):
Option A: Centralized Single-Machine Sharing (Ollama)
If you already own one expensive workstation that can fit an entire AI model on its own, Ollama lets you set OLLAMA_HOST=0.0.0.0 so weaker laptops or phones can open a chat window over Wi-Fi. The Catch: Ollama does not combine RAM across devices. If you want to run an 18 GB 30B model, a 42 GB 70B model, or a 66 GB 120B model—and your primary laptop only has 16 GB of RAM (~10.5 GB free after OS overhead)—centralized sharing fails with an Out-of-Memory error while your other laptops, PCs, and phones contribute 0 GB of memory.
Option B: Developer Command-Line Frameworks (Exo & llama.cpp RPC Mode)
Open-source developer projects like Exo and llama.cpp RPC mode split model layers across multiple machines, but come with major cross-platform and usability catches:
• Exo: Built primarily for macOS (Apple Silicon) and Linux. It has no native Windows desktop installer (requiring Linux containers or WSL workarounds), mobile support is experimental rather than delivered via standard App Store or Google Play clients, and version mismatches between machines cause nodes to drop out.
• llama.cpp RPC Mode: Compiles on Windows, macOS, and Linux, but requires manually opening terminal windows on every worker machine, launching command-line servers, and typing terminal flags with IP addresses and ports. There is no click-and-play app interface, no dedicated router hub, no zero-install USB stick, and no official iOS or Android app store client.
Option C: Click-and-Play Appliance Pooling (RAMDeck Hub + Node USB + App Store Apps)
Created by The TradeMAV Team (authors of Pool the RAM You Already Own, ISBN 979-8-16746-277-9), RAMDeck eliminates terminal commands entirely. You plug the palm-sized RAMDeck Hardware Hub into your router via Ethernet, and connect any combination of Windows PCs, Macs, Linux machines, iPhones, iPads, and Android phones in 60 seconds through its local LAN IP address.
2. Current Operating System & Device Support Breakdown
Here is the verified side-by-side compatibility breakdown across all five consumer operating systems:
| Platform / Capability | Ollama (Centralized Sharing) | Exo (Developer CLI) | llama.cpp RPC (Manual CLI) | RAMDeck Hub ($159 + Free Node USB) |
|---|---|---|---|---|
| Combines RAM Across Devices? | ❌ No (1 machine only) | ✅ Yes | ✅ Yes | ✅ Yes (Up to 120B+ models) |
| Click-and-Play App Interface? | ✅ Single-machine only | ❌ Requires CLI setup | ❌ Terminal commands only | ✅ Yes (Zero terminal commands) |
| Windows 10/11 (CUDA & CPU) | Single machine only | ❌ Not natively supported (WSL/container) | ⚠️ Manual terminal server | ✅ Native 1-Click Installer + Node USB |
| macOS (Apple Silicon & Intel) | Single machine only | ✅ Apple Silicon MLX | ⚠️ Manual terminal server | ✅ Native 1-Click DMG + Node USB |
| Linux (Ubuntu/Debian CUDA & CPU) | Single machine only | ✅ CLI install | ⚠️ Manual terminal server | ✅ Native 1-Click DEB + Node USB |
| iOS / iPadOS (iPhone & iPad) | ❌ Pools 0 GB RAM | ⚠️ Experimental (No App Store app) | ❌ No App Store app | ✅ Official Apple App Store App |
| Android Phones & Tablets | ❌ Pools 0 GB RAM | ⚠️ Experimental (Termux CLI) | ⚠️ Requires Termux CLI compile | ✅ Official Google Play Store App |
| Zero-Install Portable USB Mode | ❌ No | ❌ No | ❌ No | ✅ Official “Unplug & Walk” Node USB (0 bytes on disk) |
| Dedicated 24/7 Router Hub | ❌ Host PC must stay on | ❌ Host PC must stay on | ❌ Host PC must stay on | ✅ Dedicated Ethernet Hub Included |
3. Reality Check on Distributed AI Speed: Dense Models vs. 30B Mixture-of-Experts (MoE) Over Wi-Fi
A common question about distributed inference is whether splitting a model across standard home Ethernet or Wi-Fi slows down generation speed. The answer comes down to two principles verified across our test bench:
1. Stationary Weights, Moving Signals: Inside a RAMDeck cluster, multi-gigabyte model weights stay parked inside each device’s fast local RAM. What travels across your router on each generated word is only a tiny mathematical handoff signal (typically 8 KB to 64 KB per hop).
2. Mixture-of-Experts (MoE) vs. Dense Models:
- 30B Mixture-of-Experts (MoE) at 28.0 to 39.0 Tokens/Sec: Modern MoE models like
Qwen 30B A3BandQwen3-Coder 30Brequire ~18 GB of pooled RAM to load (which crashes a single 16 GB laptop), yet activate only 3 billion specialist parameters (~1.8 GB) per word. Pooled across a 16 GB M3 MacBook + iPhone 16 + iPhone XS over standard home Wi-Fi,Qwen 30B MoEstreams at 28.0 tokens per second (watch video), and reaches 38 to 39 tokens per second on desktop clusters (watch video). - 27B & 120B Models Unlocked (From Impossible to Possible): Heavy Dense models like
Qwen 3.8 27B Denseread 100% of parameters on every word, running at 1.92 tokens per second across a 2019 Acer laptop (12 GB RAM, no GPU), a Windows Mini PC, a Mac mini, and a Samsung Galaxy S24+ (watch video), while120Bmodels run at 1.37 to 3.1 tokens per second across 5 pooled computers (watch video)—turning a fatal Out-of-Memory crash on a single PC into a working, 100% offline cluster.
Read the Complete Book or Get the RAMDeck Hub
Read our full 10-chapter practical guide Pool the RAM You Already Own (ISBN 979-8-16746-277-9) online for free at ramdeck.com/book, or back the plug-and-play RAMDeck Hardware Hub on Indiegogo ($159 launch special through November 9, 2026 + 1 FREE $29 Official Node USB).