Impact of 4-bit Quantization on Llama-3 LongBench Accuracy Under Strict Edge Memory Constraints

Abstract

Abstract: Context lengths of Large Language Models (LLMs) have exploded in recent years, with 128k-token context becoming a standard and million-token context becoming a reality. Efficiently supporting long-context inference remains challenging as the memory that must be allocated in key-value (KV) cache for a generation scales with its context length, limiting the number of long-context requests that can be served concurrently under a given memory budget. KV cache compression can mitigate this issue by removing under-utilized KVs from each attention head's cache and reducing its memory footprint. Highe

Research Question

How does 4-bit quantization impact LongBench accuracy for Llama-3 compared to full-precision baselines when evaluated under strict edge memory constraints?

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