Unverified50% confidenceConceptExact time
So-called "the model runs on-device" usually means deploying a parameter-compressed model (possibly quantized, such as 4-bit or 8-bit) locally, with inference performed by the device's own CPU, GPU, or neural engine rather than via a remote server.
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Long-term
Relevance
10/24/2026
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VerifiedAn inference runtime is the execution layer between the model weight files and the final output, responsible for mapping the model computation graph to hardware, including memory allocation, operator fusion, quantized computation, and batch scheduling.77% similarUnverifiedLocal inference means the model or algorithm runs on the user's own device, with the image never leaving the device from reading to output, at the cost of computing power requirements on local hardware, and the upper limit of processing speed depends on the machine's configuration.73% similarUnverifiedLocal deployment of large models moves model inference that originally occurred on cloud servers to one's own computer, with model weights stored locally and the primary inference computation occurring on the local CPU, GPU, or accelerator hardware.73% similarUnverifiedOn-device models refer to small models whose parameter scale is compressed enough to allow real-time inference on phone chips, typically using quantization, pruning and distillation to reduce compute and memory usage.72% similarUnverifiedIn serverless inference mode, model instances are started on demand and destroyed after use, with the platform handling elastic scaling; in dedicated inference mode, users reserve fixed GPU instances for a specific model that run continuously71% similar
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