Ternary-Quantized AI, From Silicon to Models
Ternary AI infrastructure for the real world.
TaoTern builds vertically integrated AI systems for a future where inference must run closer to the data, under tighter power budgets, and with full operational control.
We combine custom acceleration hardware, open software, and ternary-optimized models to make high-performance LLM deployment practical beyond the data center.
Why TaoTern
Cloud AI was not designed for the environments that need intelligence most.
Most modern AI infrastructure assumes abundant power, centralized compute, and constant cloud access. That assumption breaks in enterprise, industrial, embedded, and sovereign deployments.
TaoTern is building the alternative: efficient AI systems that deliver serious inference capability where power efficiency, latency, and data control are non-negotiable.
Too much power
General-purpose compute wastes energy on inference workloads that should be purpose-built.
Too much dependence
Cloud-first deployment leaves critical systems exposed to latency, cost, and connectivity constraints.
Too little control
Sensitive inference often needs to run on-device or on-prem, not inside someone else's infrastructure.
Platform
One stack, designed together for efficient sovereign inference.
TaoTern controls the critical layers of deployment performance: custom hardware, open execution software, and model architectures built specifically for ternary quantization.
That vertical integration lets us co-design around throughput-per-watt, predictable latency, and deployability in places where GPUs and cloud assumptions do not hold.
Silicon
TaoTern TPU
A Ternary Processing Unit engineered for quantized LLM inference, delivering strong real-time performance under a 3W power envelope.
Software
Open runtime ecosystem
Firmware, drivers, and a PyTorch-compatible execution layer built to make optimized deployment practical rather than experimental.
Models
Ternary-optimized LLMs
Custom architectures and transformation workflows designed for compact, efficient inference in edge, embedded, and enterprise environments.
Performance
Efficiency that changes where AI can actually run.
10x
TaoTern's optimization approach can transform existing LLMs into SSM-based architectures for up to 10x inference speedup, while our hardware path targets an order-of-magnitude improvement in energy efficiency over CPU-based inference.
Platform
Power
Efficiency
Throughput
Best fit
Modern CPU
30-80 W
30 GOPS/W
Low-Moderate
General compute
TaoTern TPU
3 W
300 GOPS/W
~50 tok/s
Edge, embedded, enterprise
Proof of Capability
A working baseline model that shows the architecture direction.
TaoTern's baseline model is a 30M-parameter, SSM-based, ternary-quantized LLM built for compact deployment and predictable inference behavior.
It is not the endpoint of the company. It is proof that the stack works, and a foundation for customer-specific fine-tuning, distillation, and transformation workflows.
Linear-time inference
SSM architecture supports efficient execution with tighter latency predictability.
Compact footprint
Ternary quantization reduces compute and memory pressure for deployment beyond the cloud.
Adaptable workflow
Serves as a base for custom training, distillation, and model transformation.
Use Cases
Built for environments where data, power, and latency are strategic constraints.
Edge and embedded systems
Run practical language inference in devices and systems that cannot tolerate GPU-scale power draw.
On-prem enterprise AI
Keep inference close to sensitive data with controlled infrastructure and long-term cost discipline.
Offline and sovereign deployment
Support mission-critical workloads where cloud connectivity is limited, undesirable, or impossible.
How We Work
From deployment constraints to production inference.
Discovery
We define model goals, deployment boundaries, power budgets, and latency targets with your team.
Model strategy
We train, fine-tune, distill, or transform existing models into more efficient architectures.
Deployment
We deploy remotely or on-site with support across inference hosting and TPU server rollouts.
Contact
Build efficient AI where your data lives.
TaoTern is building the infrastructure layer for real-world inference: efficient, sovereign, and deployable outside the hyperscaler template.