Data movement is expensive.
Fetching and moving weights consumes energy and memory bandwidth. Faster arithmetic alone cannot remove that cost.
Native ternary AI acceleration
Ternary AI.
Native silicon.
We’re developing an AI inference chip built around ternary intelligence—uniting memory and compute to make more AI possible within real-world power limits.
See our approachAustin, Texas · Memory + compute
Three states. One integrated vision.Why hardware needs to change
01 / The problem
The model has changed.
The hardware needs to follow.
Ternary models express their weights as −1, 0 and +1. Software such as bitnet.cpp runs those models using binary storage formats and conventional processors.
Our mission is to close that gap at the silicon level—and tackle the cost of storing, moving and using model weights.
Ternary takes a binary route.
On conventional processors, ternary software packs weights into binary formats and uses specialized kernels to execute them. We’re targeting the next step: hardware designed around the same three-value model.
Power sets the ceiling.
On-device AI shares a finite battery and thermal budget with the rest of the device. The challenge is to deliver more useful inference within those limits.
02 / Why ternary
Three values.
A simpler starting point.
For a ternary weight, multiplying an input means taking its negative, making a zero contribution, or keeping it unchanged. That structure changes the work at the heart of inference.
Our focus is to build memory and compute around it, from how a weight is stored to how it is used.
−1
Subtract−1 × x = −x
0
Zero contribution0 × x = 0
+1
Add+1 × x = x
Ternary software has opened a new direction for AI. Trinary.ai is taking that direction into native hardware.
03 / Our approach
Memory + compute + software
Build around the model.
Across the chip.
We’re developing a native ternary AI accelerator: an inference chip designed to store ternary weights and execute the operations that use them as one coordinated system.
The goal is straightforward: reduce the work required for inference, so more intelligence fits into the device’s power budget.
Store natively.
Develop a physical three-state foundation for ternary weights, aligning how the model represents information with how the chip stores it.
Build for ternary math.
Design inference datapaths around ternary operations, concentrating hardware on the work the model actually needs.
Design it together.
Co-design memory, compute and software to reduce data movement and optimize energy per useful inference.
What we’re building toward
- Lower inference energy
- Longer battery life
- More AI on device
In development
Our accelerator is in development; measured performance will be shared as hardware is validated.
04 / Use cases
Intelligence where
it happens.
We’re focused on AI that earns its place in the device: responsive, local and efficient enough for sustained use.
01 / On-device AI
Local language assistants
Bring language, voice and contextual assistance closer to the user. Our target is responsive local inference that makes better use of limited memory and power.
02 / Battery-powered vision
Drones, glasses and cameras
Drones, smart glasses and battery-powered cameras need useful on-device vision—from navigation to scene understanding and event detection. We’re targeting lower inference energy and longer operation between charges.
The battery-life gain depends on inference’s share of total device power, alongside sensors, radios, displays and drone propulsion.
03 / Model serving
Larger ternary models
Scale efficient inference beyond the edge. We’re targeting the cost of storing, moving and processing model weights—the pressure points in serving larger models.
04 / Hardware and software
One system, built together
Develop memory, compute and model execution together. Evaluate complete workloads to turn device advances into useful inference performance.
The measure of progress: useful model accuracy, latency and energy per inference, tested against efficient implementations of the same workload.
Build with us05 / Company
Built in Austin.
Thinking beyond binary.
A clear mission.
Deep engineering.
Trinary.ai is an Austin-based semiconductor company developing native ternary AI acceleration across memory and compute.
We believe the next generation of AI deserves hardware designed around the way it works. We’re building toward that future, from device physics to inference.
Founder · Austin, Texas
Vinny Lingham
Vinny Lingham, Co-Founder and Chairman of Praxos Capital, is a recognized thought leader in crypto and digital identity who brings decades of experience as an entrepreneur, investor, and blockchain pioneer. He founded Civic and Gyft (acquired by First Data), was a General Partner at Multicoin Capital, and an early investor in leading projects such as Solana, Filecoin, and Render.
06 / Contact
Build the next direction
in AI hardware.
For research, industry and company inquiries.