Frontier AI. Compressed 10×. On the device you already own.
Frontier models are 16-bit. We compress them to ternary 1.58-bit {−1, 0, +1} — and recover capability.
Flagship · Research milestone
Wisteria. Frontier capability on consumer hardware.
Our 1.58-bit ternary language model series, recovered from frontier FP16 parents via continual pretraining and distillation. Wisteria-122B-A10B is the current proof — a 122-billion parameter MoE in 24 GB, running offline on a MacBook. The series scales from 125B to 1T parameters.
125B–1T
Model range
1.58-bit
Ternary weights
24 GB
Wisteria-122B footprint
Offline
Consumer hardware
"Health data is the most private data people own. We're proving that using state-of-the-art AI doesn't require sending it to the cloud."
MALIK AHMED
FOUNDER & CEO
Research
Research.
Wisteria-122B-A10B: Frontier capability on consumer hardware
FlagshipParams Is All You Need: Why We Killed Our 0.8B and What a Red Bull Taught Us
InsightBoba-0.8B: On-Device Food Nutrition Estimation at Scale
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VisionHow a 10-Agent Swarm Fixed Our Biggest Bottleneck
AgentsPipeline vs Direct: Lessons from a 5-Model Architecture
ArchitectureCross-Model Alignment Without Retraining
Alignment
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