The Kinetic Processing Unit (KPU) by LantzLabs. A proprietary hardware-software co-design that completely bypasses the memory wall, executing frontier Hugging Face models natively on LUTs with zero DSP reliance and ultra-low dynamic power.
Our offline compiler compresses the critical structural geometry of the matrix before it hits the silicon. We achieve a deterministic 70% lossless physical compression over standard FP16. We shrink the model footprint by 70% without lobotomizing its reasoning capabilities.
We solve the exact memory bottleneck crippling long-context AI. Our streaming, zero-stall RTL achieves a dynamic 40x-80x compression ratio on the live KV Cache. For the edge, it means running massive context windows on limited DDR4. For the enterprise, it means serving more concurrent users on the exact same hardware footprint.
Standard AI hardware burns massive dynamic power constantly toggling MAC arrays. We replaced standard matrix math with a highly-gated, event-driven RTL pipeline. Our logic only draws power exactly when a bit flips during a token generation. The result is ultra-low dynamic power execution that fits perfectly into severe edge thermal envelopes.
We aren’t bound by the data center, and we aren't bound by a booth. We’ve built an edge-to-cloud inference standard that is so hyper-efficient, we are bringing it to Nuremberg in a suitcase.
Throughout EmbeddedWorld, our lead architects conducted private, live demonstrations of the KPU running a frontier-capable model strictly on a 10-watt AMD Kria™ KR260 with just 4GB of shared DDR4 memory. Watch our zero-DSP RTL dynamically multiply the effective bandwidth of a standard edge bus, proving our compression live without a single OOM error.
Our architecture relies on standard programmable logic, not proprietary DSPs. This means the exact same multiplier-free inference engine running on our $400 edge board scales natively to the high-bandwidth buses of enterprise-class silicon across any hardware ecosystem.
