Successfully loaded and ran Llama 4 Scout (109 billion parameters, 57.3 GB) distributed across two consumer desktop PCs using an open-source RPC protocol over a standard home network. Also ran 72B and 30B models at interactive speeds.
| Metric | Value | Notes |
|---|---|---|
| Model | 109B parameters (Llama 4 Scout MoE) | 49 layers, 16 experts |
| Model Size | 57.3 GB | Distributed across 2 machines, 3 memory pools |
| Total Time | 86 seconds | Prompt + generation |
| Output | "Four." (asked "What is 2+2?") CORRECT | |
| Status | PROVEN 100B+ on consumer hardware — MILESTONE | |
| Model | Params | Speed | Use Case |
|---|---|---|---|
| Athene-v2 | 72B | 0.21 tok/s | Deep analysis, batch reasoning |
| Qwen3-Coder | 30B | 7.5 tok/s | Interactive coding & analysis |
At Q4 quantization (4 bits per weight), every gigabyte of consumer RAM holds approximately 1.6 billion neural network parameters. This ratio is the key.
| Model | Parameters | Size (Q4) | Params/GB |
|---|---|---|---|
| Qwen3 8B | 8B | 4.9 GB | 1.63B/GB |
| Qwen3-Coder 30B | 30B | 17.3 GB | 1.73B/GB |
| Athene-v2 | 72B | 44.2 GB | 1.63B/GB |
| Llama 4 Scout | 109B | 57 GB | 1.91B/GB |
| Scale | Nodes | Memory | Parameters | Equivalent To |
|---|---|---|---|---|
| PROVEN | 2 | 88 GB | 140 B | Llama 3 70B class |
| PHASE 2 | 3 | 264 GB | 422 B | Llama 3 405B class |
| PHASE 3 | 9 | 1,228 GB | 1.96 T | Beyond GPT-4 scale |
| Community (50 homes) | 50 | 7,200 GB | 11.5 T | Beyond any public model |
| Municipal (500 nodes) | 500 | 72 TB | 115 T | Sovereign city-scale AI |
| National (10,000) | 10,000 | 1.4 PB | 2,240 T | Sovereign AGI infrastructure |
Just as Bitcoin mining pools let anyone contribute hash power to collectively solve blocks no single machine could, memory pooling lets anyone contribute RAM to collectively run AI models no single machine could hold.
The current AI industry runs a brutal cycle:
Raise $10B → Build data center → Train model 6 months → Stop → Deploy static → Model goes stale → Raise more → Build bigger → Train again → Kill old one → Repeat
Each generation costs MORE. Only 3-4 companies on Earth can play.
A family's gaming PCs and old laptops become a private AI cluster. Medical questions, homework help, financial analysis — all running locally. No data leaves the house. No subscription required.
50 homes contributing one node each = 7,200 GB = 11.5 trillion parameters. A neighborhood running models that exceed GPT-4. Community-owned, community-governed.
10,000 consumer nodes = 1.4 petabytes = 2.2 quadrillion parameters. Sovereign AI capability no sanctions can touch, no API can revoke. Total cost: less than a single fighter jet.
AGI shouldn't be a product you subscribe to. It should be infrastructure you own. Like electricity. Like water. Distributed meshes make frontier AI a public utility, not a private monopoly.
| Component | Status | Description |
|---|---|---|
| Distributed Memory Mesh | NOVEL | Persistent AI memory with semantic embedding, federated recall, and cross-machine replication. Knowledge grows with every interaction. |
| Unified Memory Pool | NOVEL | Multiple machines' RAM + VRAM treated as one addressable space. Single API searches all nodes and merges results by relevance. |
| Autonomous Brain (Mesh Cortex) | NOVEL | 7-subsystem persistent brain: self-healing watchdog, knowledge feedback loop, autonomous task queue, model auto-selection, session continuity, multi-model consensus, and persistent identity. |
| GPU Compute via RPC | ENGINEERING | AMD Vulkan GPU serving model layers through a distributed mesh protocol. Consumer GPU doing work that used to require enterprise hardware. |
| 600+ Agent Command Center | INTEGRATION | Full orchestration stack with automated deployment, remote machine control, and real-time mesh monitoring dashboard. |
| Parameters | 109 billion |
| Size | 57.3 GB (Q4_K_S) |
| Distribution | GPU + CPU + Remote RPC |
| Generation | 0.20 tok/s |
| Total Time | 86 seconds |
| Parameters | 72 billion |
| Size | 44.2 GB (Q4_K_M) |
| Distribution | GPU + Remote RPC |
| Generation | 0.21 tok/s |
| Quality | Frontier reasoning |
| Parameters | 30.5 billion (MoE) |
| Size | 17.3 GB (Q4_K_M) |
| Distribution | GPU + local RAM |
| Generation | 7.5 tok/s |
| Quality | Interactive speed |