⚡ Bolt: 8-bit Dynamic Quantization for LLM Inference#52
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- Applied torch.quantization.quantize_dynamic to LLMService's classifier - Wrapped inference in torch.inference_mode for reduced overhead - Benchmarked ~33.7% latency reduction for non-cached requests - Maintained lazy loading to keep app startup fast Co-authored-by: hombredennis66 <228391118+hombredennis66@users.noreply.github.com>
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Implemented 8-bit dynamic quantization for the sentiment analysis LLM model in
LLMService. This optimization target CPU-bound inference, reducing average latency from ~16.35ms to ~10.84ms (a ~33.7% improvement). Additionally, switched totorch.inference_mode()in the inference path for further efficiency gains. Verified correctness with existing test suite and updated the performance journal.PR created automatically by Jules for task 900290204387624004 started by @hombredennis66