Tamp your models.
Run them anywhere.
We compress AI models to run fast on CPUs and on-device.
Building with teams who care about cost, latency, and on-device AI
Engineered for efficiency.
Our core technique enables high-performance inference on restricted hardware.
CPU-first performance
Target real CPU bottlenecks, not just smaller weights. Run LLMs on commodity hardware.
Intelligent Optimization
Automatically identify and optimize redundant computations without retraining from scratch.
Pairs with quantization
Stack architecture-aware compression with standard pruning and quantization for max gains.
Quality-aware
Evaluation harness and regression checks per task to ensure model fidelity.
Deploy anywhere
Run on commodity CPU fleets, edge devices, and privacy-sensitive on-prem environments.
Developer tooling
SDK/CLI + detailed reports showing speed/memory/quality tradeoffs.
How it works
Profile
Identify bottlenecks in your model architecture.
Compress
Advanced algorithms reduce model size while maintaining accuracy.
Optimise
Optional quantization within constraints.
Export
Validate and ship to target CPU.
Real impact on inference.
We drastically reduce the computational cost of running large models, making them viable for production on standard hardware.
* Results vary by model/task. Report provided per run.
Benchmark: Llama-3-8B (CPU)
Make GPU-class models
CPU-friendly.
Send a model + target hardware. We’ll return a compressed artifact and a performance report.