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QKVShare framework enables efficient quantized KV-cache handoff for on-device LLMs

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have developed QKVShare, a framework designed to improve the efficiency of transferring latent context between agents in multi-agent LLM systems operating on edge devices. This approach utilizes quantized KV-cache handoff, combining token-level mixed-precision allocation with a CacheCard representation and a HuggingFace-compatible injection path. Experiments with Llama-3.1-8B-Instruct on GSM8K problems demonstrated that adaptive quantization is competitive under repeated handoffs and significantly reduces handoff latency compared to full re-prefill. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Potentially enables more efficient on-device multi-agent LLM systems by reducing context transfer overhead.

RANK_REASON Academic paper detailing a new framework for LLM context transfer.

Read on arXiv cs.AI →

COVERAGE [2]

  1. arXiv cs.AI TIER_1 · Pratik Honavar, Tejpratap GVSL ·

    QKVShare: Quantized KV-Cache Handoff for Multi-Agent On-Device LLMs

    arXiv:2605.03884v1 Announce Type: new Abstract: Multi-agent LLM systems on edge devices need to hand off latent context efficiently, but the practical choices today are expensive re-prefill or full-precision KV transfer. We study QKVShare, a framework for quantized KV-cache hando…

  2. arXiv cs.AI TIER_1 · Tejpratap GVSL ·

    QKVShare: Quantized KV-Cache Handoff for Multi-Agent On-Device LLMs

    Multi-agent LLM systems on edge devices need to hand off latent context efficiently, but the practical choices today are expensive re-prefill or full-precision KV transfer. We study QKVShare, a framework for quantized KV-cache handoff between agents that combines token-level mixe…

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