The Deci platform optimizes your deep learning models to maximize the utilization of any hardware, enabling efficient inference without compromising accuracy.Book a Demo
The demands of advanced IT infrastructure technologies, such as deep-learning training and inference, will force most enterprises to either update existing data centers or build new ones. This increase in workload is driving the demand for hardware, software, disaster recovery, continuous power supplies, networking and cooling costs. For example, deep-learning inference requires the purchase of new servers with expensive AI-dedicated GPU chips. Demand for deep-learning inference workloads can dramatically increase your data center total-cost-of-ownership (TCO) and cut your product’s profitability.
Connecting your model to the platform and using the AutoNAC technology, will enable a cost-effective deep learning-based application with model optimized throughput for your target hardware. As a result, you can maximize the utilization of your data center hardware, consider switching to cheaper hardware and run multiple models on the same hardware.
All without compromising on performance or accuracy!
Boost your trained model’s throughput/latency for any hardware, with Deci’s AutoNAC algorithmic optimization, without compromising on accuracy.
CPU and GPU
"Intel and Deci partnered to break a new record at the MLPerf benchmark, accelerating deep learning by 11x on Intel’s Cascade Lake CPU. That’s amazing!
Deci’s platform and technology have what it takes to unleash a whole new world of opportunities for deep learning inference on CPUs."