Bring Advanced AI to Any Device
Multiverse Computing enables advanced AI on devices that previously couldn't support it—without hardware redesign or cloud dependency.
Ultra-compressed nanomodels
Small enough to run on devices with just a few MBs of memory.
Fast, low-power inference
Optimized for battery-powered and remote devices with strict energy constraints.
More AI on the same hardware
Add advanced AI capabilities without redesigning or upgrading the device.
Fully offline, on-device processing
Zero latency and maximum data privacy with no cloud dependency.
Broad hardware compatibility
Supports CPUs, GPUs, AI accelerators, FPGAs, and other embedded platforms.
Improved bill of materials (BOM)
Enables the use of cheaper, more efficient chips while maintaining AI performance.
Very limited compute power
Embedded devices cannot run traditional large AI models.
Constrained memory capacity
Insufficient memory to load or store high-parameter models.
Strict energy constraints
Battery-powered and remote devices require ultra-efficient inference.
Critical latency requirements
Cloud processing is not viable for real-time or safety-critical use cases.
More intelligence without higher cost
Devices must increase AI capabilities without raising hardware or BOM costs.
“We conducted extensive technical benchmarking and were very impressed with the results: decrease in time to first token, increase in token throughput, and models that are cheaper to run (50–80% cost and energy savings)”
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