Recently, considerable efforts have been directed towards compressing Large Language Models (LLMs), which showcase groundbreaking capabilities across diverse applications but entail significant deployment costs due to their large sizes. Meanwhile, much less attention has been given to mitigating the costs associated with deploying multiple LLMs of varying sizes despite its practical significance. Thus, this paper introduces any-precision LLM, extending the concept of any-precision DNN to LLMs. Addressing challenges in any-precision LLM, we propose a lightweight method for any-precision quantization of LLMs, leveraging a post-training quantization framework, and develop a specialized software engine for its efficient serving. As a result, our solution significantly reduces the high costs of deploying multiple, different-sized LLMs by overlaying LLMs quantized to varying bit-widths, such as 3, 4, ..., $n$ bits, into a memory footprint comparable to a single $n$-bit LLM. All the supported LLMs with varying bit-widths demonstrate state-of-the-art model quality and inference throughput, proving itself to be a compelling option for deployment of multiple, different-sized LLMs.

Effectively modeling relationships between distant elements in a long input sequence is an important task that remains challenging to this day. The state-of-the-art models for processing sequential data are self-attention-based transformer models. However, the computational complexity of self-attention is quadratic to the input sequence length, which often becomes the limiting factor in scaling the sequence length. Recently, state space model (SSM)-based global convolution models, which replace attention with convolution, have been found to be effective for modeling long sequences, with a sub-quadratic complexity using Fast Fourier Transform (FFT). However, they show sub-optimal performance on data-parallel accelerators like GPU, due to the regions of extremely low compute utilization with memory bandwidth-bound operations. To address this inefficiency, this paper proposes the Vandermonde matrix Generating Accelerator (VGA), a custom accelerator that performs FFT-based convolution in an area/power-efficient manner. VGA introduces Complex number Compute Units (CCUs) to fully utilize the high on-chip SRAM bandwidth, and parameters are generated on the fly to drastically reduce the required SRAM capacity. VGA achieves 76×(48×) higher area (power) efficiency than NVIDIA A100 GPU when executing the global convolution operator of H3, a state-of-the-art SSM-based model.