Successfully loaded and ran Qwen3-235B (235 billion parameters, 79.8 GB) distributed across two consumer desktop PCs using a 4-way memory split. Also proved 141B Mixtral, 109B Llama 4 Scout, 72B Athene-v2, and 30B Qwen3-Coder at interactive speeds.
| Metric | Value | Notes |
|---|---|---|
| Model | 235B parameters (Qwen3-235B-A22B MoE) | 95 layers, 22B active per token |
| Model Size | 79.8 GB | Distributed across 2 machines, 4 memory pools |
| Speed | ~0.14 tok/s (~7 sec/tok) | MoE: only 22B active per token — FASTER than 141B dense despite 94B more total params |
| Output | "Okay, the user wants me to say hello..." REASONING | |
| Live Validation | Pre-market: called ES BEARISH, SHORT at open — CORRECT. ES opened down, waterfall to 7089. | |
| Overnight Test | 5 autonomous tests, 0.35-0.59 tok/s, 94% uptime, 8 hours unattended | |
| Status | RECORD 235B on consumer hardware — LIVE VALIDATED | |
| Model | Params | Speed | Use Case |
|---|---|---|---|
| Llama 4 Scout | 109B | 0.20 tok/s | 100B+ proven |
| 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 |
| Pre-market | BEARISH — CORRECT |
| Open direction | Gap down — CORRECT |
| Updated target | 7085 by 2PM — hit early |
| Trade call | SHORT — CONFIRMED by War Room 95% |
| IHS neckline | EXACT (7160.5) |
| Fractal targets | 4/4 hit + exceeded |
| BWB BEAR | 40 min early |
| KILL_CHAIN | −25 pt crash caught |
| Duration | 8 hours unattended |
| Tests run | 5 scheduled analyses |
| Peak speed | 0.59 tok/s (4 AM) |
| Mesh uptime | 94% |
The same mesh protocol proven on live trading applies to any domain requiring distributed AI:
Family devices pool memory into a private 235B brain. Medical questions, homework, financial analysis — all local. Voice assistants that remember context via persistent memory. Phone queries the home mesh over WiFi.
Multiple CPUs inside one robot as a distributed nervous system. Head=reasoning (128GB), Spine=coordination (64GB), Limbs=reflexes (8GB each). Same RPC protocol. Touch hot = instant reflex. "Pick up the cup" = brain plans, spine coordinates, hand adjusts. 256 GB internal = 409B parameters per robot.
8B on-device for millisecond reflexes. 235B on home mesh via 5G for reasoning about edge cases. Persistent memory remembers "this intersection was icy last winter." Fast local brain + deep home brain. <5ms local vs 500ms cloud.
Autonomous legal department: deadline tracking, auto-drafting via mesh 30B, case law research via 235B, Legal War Room debates. Filing monitor auto-analyzes new documents. Gold Road persists strategy across sessions. Pro se litigants get firm-level AI support.
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 | 235 billion |
| Size | 79.8 GB (Q2_K) |
| Distribution | 4-way: GPU + Local RPC + Remote RPC |
| Generation | ~0.14 tok/s (~7 sec/tok) |
| Response | "Okay, the user wants me to say hello..." |
| Parameters | 141 billion (dense) |
| Size | 63.1 GB (Q3_K_M) |
| Generation | 0.071 tok/s (14 sec/tok) |
| Dual-RPC | 2.6x faster than single-RPC |
| Llama 4 Scout | 109B — 0.20 tok/s |
| Athene-v2 | 72B — 0.21 tok/s |
| Qwen3-Coder | 30B — 7.5 tok/s (interactive) |