Guide to Optimizing Edge Computing Scenarios
The following combination of technologies is recommended for deployment needs in resource-constrained environments:
- Model Compression::
- utilization
Knowledge_DistillationScript in the directory to distill Qwen3-4B to version 1.7B - Perform 8bit quantization after training (for an example see
inference/quantization.py)
- utilization
- hardware adaptation::
- Enabling TensorRT Acceleration on NVIDIA Jetson Devices
- Raspberry Pi and other ARM devices need to be converted to ONNX format
- dynamic loading (computing): Combine LoRA features to load only the base model + domain adapters (
.bin(Files usually less than 200MB) - Cache Optimization: Modification
inference_dirty_sft.pyhit the nail on the headmax_seq_lenParameters to control memory footprint
Empirical tests show that the quantized Qwen3-1.7B can achieve a generation speed of 5token/s on a 4GB memory device.
This answer comes from the articleQwen3-FineTuning-Playground: a ready-to-use code base for fine-tuning Qwen3's big models.The
May not be reproduced without permission:AI productivity tools " How to optimize the inference efficiency of Qwen3 fine-tuned models in edge device deployment scenarios?
