Deepgate
DeepGate enables AI models to run on standard CPU hardware, removing the dependency on specialised GPUs. This reduces compute costs and expands the potential for AI deployment in constrained environments. By shifting AI workloads to CPUs, DeepGate makes it possible to deploy models on edge devices such as cameras, drones, and sensors without relying on cloud infrastructure.
This approach addresses two key barriers to wider AI adoption: latency and cost. Running inference locally improves response times and enhances data privacy, while eliminating the need for expensive GPU infrastructure. DeepGate’s technology is particularly well suited to applications where real-time processing, energy efficiency, and on-device decision-making are critical.
This approach addresses two key barriers to wider AI adoption: latency and cost. Running inference locally improves response times and enhances data privacy, while eliminating the need for expensive GPU infrastructure. DeepGate’s technology is particularly well suited to applications where real-time processing, energy efficiency, and on-device decision-making are critical.
Summary
