Shipping LLMs (Part 3/6): Speculative Decoding vs Quantization

1. 1Apply quantization to reduce memory bandwidth
2. 2Implement speculative decoding to fix autoregression
3. 3Stack quantization and speculative decoding in the correct order
4. 4Test the optimized LLM inference for performance and cost savings
5. 5Configure the optimized model for deployment

Machine learning engineers and AI researchers on a team can benefit from this knowledge to improve the performance of their LLM models, while data scientists and product managers can understand the cost implications and potential applications

💡 Stacking quantization and speculative decoding can lead to significant cost savings and improved efficiency in LLM inference

💡 Optimize LLM inference by 3-4x with speculative decoding and quantization!