OPTIMOS PRIME — Distributed AI on Consumer Hardware

What We Proved

235B

Parameters — One Model

88 GB

Distributed Memory Pool

$0

Cloud Cost

15x

Speedup (Software Only)

The Breakthrough

235 Billion Parameters on Two Consumer Desktops

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.

235 billion parameters. Two desktops. A house in Texas. The model THINKS before answering: "Okay, the user wants me to say hello..." Four records broken in one day.

235B Memory Distribution (4-way split across 2 machines):

GPU VRAM 13.1 GB

Local RAM (RPC) 26.1 GB

Remote RAM (RPC) 40.4 GB

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/token (5x faster than 141B dense)
Output	"Okay, the user wants me to say hello..." REASONING
Status	RECORD 235B on consumer hardware — ABSOLUTE RECORD

Also proven on this mesh:

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

The Parameter Economy

~1.6 Billion Parameters Per Gigabyte

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

Consumer DDR4 RAM costs ~$1/GB and falling. Every dollar buys ~1.6 billion parameters of AI capacity. A $140 RAM upgrade adds 200+ billion parameters to the mesh.

The Scaling Math

Linear Scaling — Every Machine Adds Capacity

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

The Thesis — A New Scaling Law

Memory Pooling = Crypto Mining for AI

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.

Crypto Mining

Contribute hash power
Pool solves blocks no miner can alone
Decentralized consensus
Made finance permissionless

Memory Pooling

Contribute RAM / VRAM
Pool runs models no machine can alone
Decentralized intelligence
Makes AI permissionless

Grow, Don't Retrain

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.

The alternative: Weights handle REASONING (trained once). A distributed memory layer handles KNOWLEDGE (grows infinitely). The mesh handles CAPACITY (scales linearly with nodes). No retraining. No replacement. No billion-dollar refresh cycle.

Weights

Reasoning (train once)

Memory

Knowledge (grows forever)

Mesh

Capacity (scales linearly)

Why This Matters

For Homes

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.

For Communities

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.

For Nations

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.

For Everyone

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.

What We Built

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.

Benchmark Results

A software update alone delivered a 15x speedup — same hardware, same model. The architecture has headroom.

235B Qwen3 (RECORD)

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..."

141B Mixtral 8x22B

Parameters	141 billion
Size	63.1 GB (Q3_K_M)
Generation	0.027 tok/s
Response	"Hi there!"

109B / 72B / 30B

Llama 4 Scout	109B — 0.20 tok/s
Athene-v2	72B — 0.21 tok/s
Qwen3-Coder	30B — 7.5 tok/s (interactive)