Cut Inference Power 6.6× Per Rack.
The Sibacus Transform eliminates GPU dependency for AI inference. Run production LLMs on commodity ARM CPUs at a fraction of the power, cost, and thermal footprint.
Per-Rack Power Budget
Your Infrastructure Challenges
Problems we solve at the silicon level, not the software layer.
Thermal Budget Exceeded
"GPU-dense racks hit 30-40kW, exceeding cooling capacity in most facilities."
The Sibacus Transform runs on ARM CPUs at ~2W/inference thread vs ~300W per GPU. Same rack, 6.6× less heat.
Unsustainable GPU Costs
"H100 instances cost $30+/hr. At scale, GPU inference is the largest opex line item."
Graviton4 ARM instances cost $0.94/hr with comparable throughput per dollar. 33× cost reduction per million tokens.
Carbon & Regulatory Pressure
"EU, Singapore, and ASEAN sustainability mandates are tightening. PUE alone is not enough."
Shift-and-add compute uses 6.6× less energy per operation. Directly reduces Scope 2 emissions at the silicon level.
Infrastructure Economics
| Metric | GPU Baseline | Sibacus Transform | Reduction |
|---|---|---|---|
| Cost per 1M tokens | $1.67 | $0.05 | 33× |
| Instance cost/hr | $31.22 (p5.xlarge) | $0.94 (r8g.4xlarge) | 33× |
| Power per inference thread | ~300W | ~2W | 150× |
| Rack density (concurrent users) | ~8 per rack | ~200+ per rack | 25× |