KVNAND: Efficient On-Device Large Language Model Inference Using DRAM-Free In-Flash Computing

Abstract--Deploying large language models (LLMs) on edge devices enables personalized agents with strong privacy and low cost. However, with tens to hundreds of billions of parameters, single-batch autoregressive inference suffers from extremely low arithmetic intensity, creating severe weight-loading and bandwidth pressures on resource-constrained platforms. Recent in-flash computing (IFC) solutions alleviate this bottleneck by co-locating weight-related linear computations in the decode phase with flash, yet still rely on DRAM for the key-value (KV) cache. As context length grows, the KV cache can exceed model weights in size, imposing prohibitive DRAM cost and capacity requirements. Attempts to offload KV cache to flash suffer from severe performance penalties. We propose KVNAND, the first DRAM-free, IFC-based architecture that stores both model weights and KV cache entirely in compute-enabled 3D NAND flash. KVNAND addresses the fundamental performance challenges of flash under intensive KV cache access by leveraging IFC for all memory-bound operations to reduce data transfer overhead, introducing head-group parallelism to boost throughput, and employing page-level KV cache mapping to align token access patterns with flash organization. In addition, we propose a design space exploration framework that evaluates discrete and compact KVNAND variants to balance weight and KV placement, automatically identifying the optimal design trade-off. These techniques mitigate latency, energy, and reliability concerns, turning flash into a practical medium for long-context KV storage. Evaluations on MHA 7B and GQA 70B LLMs show that KVNAND achieves 1.98 /1.94 /2.05 geomean speedup at 128/1K/10K-token contexts compared to DRAMequipped IFC designs and addresses out-of-memory failures at 100K context length. As Large Language Models (LLMs) integrate into daily workflows, demand increases for personalized AI agents that align with user preferences, domain knowledge, and interaction styles. Deploying such agents on edge devices offers privacy, low-latency responsiveness, and cost efficiency by eliminating cloud dependency, making on-device LLMs a compelling direction for AI democratization [81]. Realizing high-quality personal LLM agents on resource-limited edge devices faces two main bottlenecks: memory capacity and bandwidth. The growing demand for long-context agentic workflows like long document analysis [35], multi-turn dialogue [84], and chain-of-thought reasoning [10] introduces the KV cache as another dominant consumer of this limited memory [19], [74]. Moreover, recent state-of-the-art (SoT A) models support extensive context lengths ranging from 128K (LLaMA3.1-70B The KV cache demand scales linearly with context length; for example, a 13B model already requires 8 GB KV memory at a 10K context [71], placing prohibitive pressure on edge resources.