APPLICATIONS
Efficient AI hardware isn't just about saving electricity. It unlocks entirely new categories of applications that are impossible with current technology.
Today's AI hardware has a fundamental constraint: power. A single H100 GPU draws 700W. A data center full of them needs its own power plant. This limits where AI can run and who can afford to run it.
Analog computing could change this equation by 100x. That's not incremental improvement— it's a phase transition that enables new applications and new markets.
TARGET MARKETS
Running AI models locally on devices—phones, cameras, sensors, vehicles—instead of sending data to the cloud. The market is huge, but current chips are too power-hungry for battery-powered devices and too expensive for commodity electronics.
10x-100x better efficiency means AI in every device, not just flagship phones. Real-time inference without cloud latency. Privacy by default.
Cloud providers spend billions on electricity for AI inference. As usage scales, energy becomes the dominant cost. Whoever can deliver the same performance at 1/10th the power wins the market.
Cooling is 40% of data center operating cost. Analog chips run cooler. Same racks, 10x the compute. Massive TCO advantage.
Robots and autonomous vehicles need to process sensor data in real-time with minimal latency. Current solutions: burn through batteries with power-hungry GPUs, or compromise on AI capability.
Microsecond latency (analog is faster). Watt-level power (longer battery life). Compact form factor (fits in robots, not just data centers).
Countries want AI capabilities without dependence on US tech giants. But building GPU-based data centers requires massive capital and power infrastructure that many nations lack.
Lower power = smaller infrastructure requirements. Mature fab nodes = no leading-edge dependency. European HQ = export-friendly jurisdiction.
WHAT WE'RE NOT TARGETING (YET)
Training large models requires high numerical precision and flexibility that analog systems don't naturally provide. Our initial focus is inference—running already-trained models efficiently. Training is a longer-term research goal.
If AI is going to be truly ubiquitous—in every device, every building, every vehicle— it needs to become 100x more efficient. Not incrementally. Fundamentally.
That's what we're building toward.
Want to discuss applications for efficient AI hardware?
founders@logorythms.com