EVA: Accelerating LLM Decoding via an Efficient Vector Quantization Architecture
Large Language Models (LLMs) have achieved impressive performance across diverse domains but remain inefficient during the autoregressive decoding phase. Unlike the prefill stage, which employs compute-bound GEMM operations, decoding executes a sequence of small GEMV-like computations that are memory-bound and underutilize modern accelerators. Weight-only vector quantization (VQ) has emerged as an effective compression technique that clusters model weights into a shared codebook and replaces the original weight matrix with low-precision indices, enabling 2-bit-level weight compression. While this approach substantially reduces model size and memory bandwidth, it still suffers from two cri...
| Authors | Bowen Duan, Cong Guo, Chiyue Wei et al. |
|---|---|
| Primary category | cs.AR |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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