When porting SLMs from development environments like Jupyter notebooks to actual devices, performance can drop 15-20% due to quantization and operator gaps in the device's NPU. Additionally, on-device inference times can slow down unpredictably due to thermal throttling and power management, unlike consistent cloud API latency.
When porting SLMs from development environments like Jupyter notebooks to actual devices, performance can drop 15-20% due to quantization and operator gaps in the device's NPU. Additionally, on-device inference times can slow down unpredictably due to thermal throttling and power management, unlike consistent cloud API latency.
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When porting SLMs from development environments like Jupyter notebooks to actual devices, performance can drop 15-20% due to quantization and operator gaps in the device's NPU. Additionally, on-device inference times can slow down unpredictably due to thermal throttling and power management, unlike consistent cloud API latency.
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