Summary
In summary, inference optimization is a critical aspect of deploying LLMs effectively. This chapter explored various optimization techniques, including optimized generation methods, model parallelism, and weight quantization. Significant speedups can be achieved by leveraging techniques like predicting multiple tokens in parallel with speculative decoding, or using an optimized attention mechanism with FlashAttention-2. Additionally, we discussed how model parallelism methods, including data, pipeline, and tensor parallelism, distribute the computational load across multiple GPUs to increase throughput and reduce latency. Weight quantization, with formats like GGUF and EXL2, further reduces the memory footprint and accelerates inference, with some calculated tradeoff in output quality.
Understanding and applying these optimization strategies are essential for achieving high performance in practical applications of LLMs, such as chatbots and code completion. The choice...
