Deploy models across your edge devices
OctoML can bring your model to life.
Faster inference at the edge
Deploying models in edge environments is complex. The remote nature of edge devices and their inherent constraints, such as battery consumption, make edge deployments a complex puzzle. OctoML solves that puzzle by deploying your model once across all your edge devices with a few simple lines of code.
Do more with less
Maximize model performance for your specific hardware. OctoML supports devices using chips manufactured by ARM, Intel, NVIDIA, Qualcomm, and Xilinx.
Build once, deploy anywhere
Build and train your model once, and OctoML will convert your model into an efficient, common format that can be executed on a number of devices.
Reduce hardware costs
See how your model performs across different hardware and pick the one that is the most optimal for the job.
We are excited about our collaboration with OctoML on Apache TVM – one of the most promising technologies that enables data scientists to run their ML models on a diverse range of Arm devices. The OctoML Platform is one of the preferred ML acceleration stacks for Arm hardware.
Mary Bennion
Sr. Manager, AI Ecosystem Arm
Read about our work
How OctoML is designed to deliver faster and lower cost inferencing
2022 will go down as the year that the general public awakened to the power and potential of AI. Apps for chat, copywriting, coding and art dominated the media conversation and took off at warp speed. But the rapid pace of adoption is a blessing and a curse for technology companies and startups who must now reckon with the staggering cost of deploying and running AI in production.
OctoML attended AWS re:Invent 2022
Last week, 14 Octonauts headed out to AWS re:Invent. We gave more than 200 demos showing how OctoML helps you save on your AI/ML journey, and gave away a dream trip to one lucky winner.