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What is the difference between LLM.int8() and AWQ?
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See plansLLM.int8() and AWQ serve different primary objectives. LLM.int8() focuses on memory efficiency by splitting activations into high-precision outliers and low-precision INT8, allowing large models to fit into memory, though it may not improve inference speed. AWQ focuses on throughput by quantizing weights to INT4 based on activation importance, which directly reduces the memory bandwidth requirement during generation, leading to faster token generation. They can be used together, but AWQ is generally the preferred choice for maximizing throughput.