Serve Your Model. 33× Cheaper.
The Sibacus Transform is a drop-in inference layer that replaces GPU compute with shift-and-add on ARM CPUs. Your model stays yours. Your API stays the same. Your costs collapse.
Monthly Serving Cost Comparison
Why Model Providers Choose Sibacus
Transform your unit economics without touching your model architecture.
33× Lower Serving Costs
Replace H100 GPU inference with ARM CPU inference. Same model, same quality, fraction of the cost. Your margin per API call increases dramatically.
Reach Sovereign Markets
Many governments require AI models to run on domestic infrastructure without GPU export dependencies. Sibacus makes your model deployable anywhere ARM CPUs exist.
Your Model, Our Engine
We never see your model weights in production. The Sibacus Transform runs as a pre-processing step that you control end-to-end in your own infrastructure.
Scale to More Users
Serve 25× more concurrent users per rack. Lower per-token costs let you offer more generous free tiers and capture market share from GPU-bound competitors.
Integration in 3 Steps
No architecture changes. No retraining. No model modifications.
Export Your Model
Export your production HuggingFace model with standard weights. No architecture changes needed.
model = AutoModelForCausalLM.from_pretrained("your-org/your-model")Apply BSA Transform
Run the Sibacus Transform to decompose weights into shift-and-add format. Takes minutes.
sibacus transform --model your-org/your-model --k 2 --output ./bsa-modelServe via API
Deploy the transformed model with our OpenAI-compatible server. Your existing clients work unchanged.
sibacus serve --model ./bsa-model --port 8080 --max-tokens 4096Validated Model Ecosystem
The Sibacus Transform works with any transformer-based model. We've validated these architectures — yours is next.