Distributed AI training on consumer GPUs worldwide. Your RTX 3080 joins thousands of others. Train frontier models without Big Tech infrastructure.
AWS, Google, Microsoft own the GPUs. They decide who can train AI, at what price, on what terms. That's not how innovation should work.
Cost to train GPT-4 class model
Waitlist for H100 clusters
Companies controlling AI compute
Every RTX card sitting idle is wasted compute. VoidAI Train connects them into a global training network. Distributed, resilient, unstoppable.
Based on DiLoCo (Google DeepMind) — proven to work across high-latency networks.
Install VoidAI client, connect your GPU to the swarm.
Get a slice of the model and training data to work on.
Train locally for N steps. No constant sync needed.
Compressed gradients sync, model improves globally.
Distributed Low-Communication training. Train across high-latency networks with minimal sync.
100-1000x compression. Only send what matters, when it matters.
Nodes can drop, rejoin, fluctuate. Training continues regardless.
Sub-nanosecond event handling. Scale to 100k+ nodes.
Direct connections between nodes. No central point of failure.
Differential privacy, secure aggregation available.
Cost reduction vs AWS/GCP
Gradient compression ratio
Potential GPUs worldwide
Your RTX 3080/4090 sits idle 20+ hours/day. Earn passive income by contributing to AI research when you're not gaming.
Est. $50-200/month per GPUTrain models without begging for cloud credits or waiting 6 months for hardware. Academic-friendly pricing.
Free tier for researchTrain your own models without VC-scale budgets. Pay only for what you use. Scale up and down instantly.
90% cost savingsOn-premise deployment. Your data never leaves your network. Custom SLAs, dedicated support.
Contact for pricingGet early access when we launch. GPU owners and AI teams welcome.
No spam. Just launch updates.