CodeGEMM: A Codebook-Centric Approach to Efficient GEMM in Quantized LLMs

• URL: http://arxiv.org/abs/2512.17970v1
• Date: Fri, 19 Dec 2025 06:16:32 GMT
• Title: CodeGEMM: A Codebook-Centric Approach to Efficient GEMM in Quantized LLMs
• Authors: Gunho Park, Jeongin Bae, Byeongwook Kim, Baeseong park, Jiwon Ryu, Hoseung Kim, Se Jung Kwon, Dongsoo Lee,
• Abstract summary: We present CodeGEMM, a codebook-centric GEMM kernel that replaces dequantization with precomputed inner products between centroids and activations stored in a lightweight Psumbook.<n>On Llama-3 models, CodeGEMM delivers 1.83x (8B) and 8.93x (70B) speedups in the 2-bit configuration compared to state-of-the-art codebook-based quantization at comparable accuracy.
• Score: 14.5426213901124
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Weight-only quantization is widely used to mitigate the memory-bound nature of LLM inference. Codebook-based methods extend this trend by achieving strong accuracy in the extremely low-bit regime (e.g., 2-bit). However, current kernels rely on dequantization, which repeatedly fetches centroids and reconstructs weights, incurring substantial latency and cache pressure. We present CodeGEMM, a codebook-centric GEMM kernel that replaces dequantization with precomputed inner products between centroids and activations stored in a lightweight Psumbook. At inference, code indices directly gather these partial sums, eliminating per-element lookups and reducing the on-chip footprint. The kernel supports the systematic exploration of latency-memory-accuracy trade-offs under a unified implementation. On Llama-3 models, CodeGEMM delivers 1.83x (8B) and 8.93x (70B) speedups in the 2-bit configuration compared to state-of-the-art codebook-based quantization at comparable accuracy and further improves computing efficiency and memory subsystem utilization.

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