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Jonah Wonkyu Yi

2 accepted papers

2024

KV Cache is 1 Bit Per Channel: Efficient Large Language Model Inference with Coupled Quantization

NeurIPS 2024poster

Efficient deployment of Large Language Models (LLMs) requires batching multiple requests together to improve throughput. As batch size, context length, or model size increases, the size of key and value (KV) cache quickly becomes the main contributor to GPU memory usage and the bottleneck of inferen…

Cited by 23SourcePDFScholar
2024

NoMAD-Attention: Efficient LLM Inference on CPUs Through Multiply-add-free Attention

NeurIPS 2024poster

Large Language Model (LLM) inference on Central Processing Units (CPU) is challenging due to the vast quantities of Multiply-Add (MAD) matrix operations in the attention computations. This paper highlights a rare gem in modern CPUs, Single-Instruction-Multiple-Data (SIMD) registers, which allows fo…