Product · TensorHyper
Structure-Aware Model Generation
Unlike pruning or distillation — which are lossy compressions — TensorHyper generates compressed AI models natively through tensor decomposition. The result: extreme compression with near-zero accuracy loss, deployable across GPU, CPU, and (in dual mode) QPU.
Validated performance
Compression Ratio
Target
>50×
Measured
~2,900×
11.69M → 4,035 parameters in validated experiment
Accuracy Loss
Target
Near-zero
Measured
0% logical loss
Compressed model matches or exceeds the original
Compute & Memory
Target
Up to 90% reduction
Measured
Up to 90% reduction
Lower-cost deployment across GPU and CPU
Hardware
Target
GPU / CPU
Measured
GPU + CPU + QPU-ready
Tensor representation is hardware-agnostic
How TensorHyper compares
Conventional compression sacrifices accuracy for size. TensorHyper builds the compressed model from the start, preserving capability.
| Method | Compression Ratio | Accuracy Loss |
|---|---|---|
| Quantization | 50–60% | 15–30% |
| Distillation | 40–50% | 10–25% |
| Tensor Networks (Multiverse) | >50% | 2–3% |
| TensorHyper (QuStruct.AI) | >50× / up to 2,900× | ~0% |
What you can build with it
Edge & on-device AI
Deploy production-grade models on CPUs and edge devices with up to 90% reduction in compute and memory.
Mission-critical enterprise AI
Zero logical capability loss makes TensorHyper safe for high-stakes financial and enterprise systems.
Quantum-ready transition
The same tensor representation runs on GPU today and migrates to QPU when paired with TensorDual-VQC.
Lower inference cost
Smaller model footprint means lower per-request cost — bending the operating-cost curve of frontier AI.