Cloud AI
Artificial intelligence models that run on remote servers, featuring large-scale parameters and rapid update capability, but requiring network connectivity and involving data upload.
Core Facts
Timeline (last 90 days)
Cloud-based solutions such as Notion AI and Copilot perform their processing on the service provider's servers, creating potential privacy exposure risks for user data during transmission and processing.
On-device AI completes inference directly on the local chip, data remains on the device throughout, response speed is faster, and it is available even without a network connection.
When using cloud-based AI models, input content is sent to a remote server for processing before results are returned, meaning data leaves the device.
Cloud-based AI typically has network latency of 100–500 ms, whereas on-device AI can achieve millisecond-level response times and complete data isolation.
Sending proprietary code to the cloud raises risks of intellectual property and trade secret leakage.
Compared to cloud-based processing, on-device AI processing can save 50–200 ms of network round-trip latency, but typically incurs a loss of approximately 2–5% in recognition accuracy due to compression techniques such as model quantization and pruning.
All Facts (7)
Cloud-based solutions such as Notion AI and Copilot perform their processing on the service provider's servers, creating potential privacy exposure risks for user data during transmission and processing.
65%UnverifiedOn-device AI completes inference directly on the local chip, data remains on the device throughout, response speed is faster, and it is available even without a network connection.
50%UnverifiedWhen using cloud-based AI models, input content is sent to a remote server for processing before results are returned, meaning data leaves the device.
50%UnverifiedCloud-based AI typically has network latency of 100–500 ms, whereas on-device AI can achieve millisecond-level response times and complete data isolation.
50%UnverifiedSending proprietary code to the cloud raises risks of intellectual property and trade secret leakage.
50%UnverifiedCompared to cloud-based processing, on-device AI processing can save 50–200 ms of network round-trip latency, but typically incurs a loss of approximately 2–5% in recognition accuracy due to compression techniques such as model quantization and pruning.
50%UnverifiedAI 本地化的本质是解耦整套云依赖，而不是替换单一组件
50%