FastDeploy passes the followingThree-tier acceleration systemImproving inference performance:
- Quantitative compression techniquesQuantization schemes including W8A16 (8-bit weights + 16-bit activation), FP8, etc., which significantly reduces the model volume and computational effort
- Decoding Optimization: Presumptive decoding techniques can predict the generation path and reduce repeated calculations; multi-token prediction realizes parallel outputs
- Hardware-level optimization: Kernel adaptation and operator optimization for different chips (e.g. RK3588's NPU)
Example of use:
Enabling quantization is simply a matter of callingmodel.enable_quantization("W8A16"), presumably decoding throughmodel.enable_speculative_decoding()Activation. Empirical tests show that these techniques can increase the inference speed of some models by a factor of 3-5.
This answer comes from the articleFastDeploy: an open source tool for rapid deployment of AI modelsThe
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