OneBit
OneBit AI converts large language models into ternary form—where each weight encodes only push, pull, or stay out of it—cutting model size to roughly one-tenth of the original while preserving most capability. These models run on standard CPUs inside phones, cameras, drones, robots, and microcontrollers, with no GPU or cloud dependency. The company offers both post-training converted models (Cloe series) and models trained from scratch in ternary space (S1.0), with published benchmarks for every release.
- Artificial Intelligence
- Hardware
- Internet of Things
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Modern language models are stored as high-precision numbers, requiring gigabytes of memory, powerful GPUs, and cloud data centers to run. This makes them inaccessible for consumer devices, industrial sensors, and microcontrollers where connectivity is limited, power is scarce, and data privacy is critical.
Solution
OneBit AI converts large language models into ternary form, where each weight encodes only push, pull, or stay out of it, cutting model size to roughly one-tenth of the original while keeping most capability. The resulting models run on standard CPUs—inside phones, cameras, drones, robots, and microcontrollers—with no GPU or cloud dependency. Live transcription, defect detection, meter reading, and wake-word recognition all execute locally, with only results transmitted. The company offers models converted post-training (Cloe series) as well as models trained from scratch in ternary space (S1.0), with a projected path to 10× size reduction while maintaining full capability. All benchmarks are published with the exact hardware and method used.
Target Audience
Primary customers are manufacturers building smart phones, TVs, cars, cameras, drones, industrial inspection systems, wearables, toys, and edge IoT devices that need embedded AI without cloud reliance.
Features
- Ternary-weight compression reducing models to ~1.94 bits per weight (Cloe 1.2) and 1.58 bits per weight (Cloe 1.1)
- Post-training ternarization with rotation and error compensation, requiring no retraining for converted models
- Quantization-aware training for models built ternary from the first step, enabling 60 MB models with 70 million parameters
- On-device inference on any CPU, eliminating GPU requirements and cloud data transfer, with models as small as 5–11 MB for speech
- Up to +24% faster decode throughput compared to latent baselines, with all-day battery operation on wearables and multi-year battery life on sensors
- Works entirely offline, with data never leaving the device; only result metadata is transmitted