New LLM Inference Algorithm for Compute-in-Flash Systems Achieves 15x KV Cache Traffic Reduction, Delivering Energy and Latency Savings for Llama-3.1-8B and Qwen-2.5-7B

September 20, 2026

arXiv Unknown

Overview

Research published on arXiv introduces a novel algorithm enabling LLM inference on Compute-in-Flash systems, mitigating memory bandwidth limitations. This method employs end-to-end integer-only quantization and a dictionary-based KV cache compression strategy. It successfully reduced dynamic KV cache traffic by 15 times for Llama-3.1-8B and Qwen-2.5-7B, yielding significant system-level latency and energy savings with minimal accuracy degradation.

In Depth

Key Findings

Pioneering research published on arXiv introduces a novel algorithm designed for LLM inference on Compute-in-Flash systems, effectively addressing the critical memory bandwidth bottleneck. This innovative technique successfully reduced dynamic KV cache traffic by up to 15 times for the Llama-3.1-8B and Qwen-2.5-7B models. This achievement translates into substantial system-level latency and energy consumption reductions, all while maintaining a remarkably limited degradation in accuracy.

Technical Details

The proposed algorithm is built upon two primary pillars. First, it employs an end-to-end integer-only quantization approach, which completely eliminates costly floating-point operations. This not only streamlines computations but also significantly reduces the memory footprint. Second, it utilizes a dictionary-based KV cache compression strategy, specifically designed to tackle the challenge of dynamic write endurance in KV caches. While the KV cache plays a vital role in LLM inference, its growth and frequent updates represent a major constraint on memory bandwidth. The dictionary-based compression dramatically curtails cache size and write volume, consequently easing memory bandwidth demands.

Background & Context

With the increasing complexity and size of LLMs, memory bandwidth during inference has become a primary bottleneck relative to GPU computational power. The KV cache, in particular, scales linearly with the number of generated tokens, making its impact pronounced in long sequence generation and multi-turn conversations. Emerging hardware architectures like Compute-in-Flash leverage the high density and low cost of NAND flash memory to enable efficient execution of large models, yet memory bandwidth challenges persisted. This research provides a crucial software breakthrough to unlock the full potential of such hardware.

Strategic Significance & Outlook

This new algorithm is poised to significantly impact LLM deployment, especially in edge devices and power-constrained environments. A 15x reduction in KV cache traffic translates into accelerated inference and substantial power savings, contributing to more sustainable AI operations. Moving forward, this technology is expected to be applied to a wider array of LLMs and hardware platforms, potentially driving efficiency across smartphone, IoT device, and datacenter-wide LLM infrastructures. This marks a critical step towards ubiquitous AI.

Source: https://arxiv.org/html/2609.16161v1

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