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SharQ Combines Activation Sparsity and FP4 Quantization for LLM Inference
SharQ is a new method addressing challenges in combining low-bit FP4 quantization with semi-structured activation sparsity for large language model inference. It tackles issues from input-dependent outliers and sparsity mask application that affect compression quality.
Efficient LLM inference requires balancing quantization and sparsity to reduce computation and memory use without degrading accuracy. SharQ's approach could improve activation compression on modern accelerators supporting these techniques.
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Score 86
Source Type arxiv
Reposts 0
Topic Quality 64
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