SparKV: An Intelligent KV Cache Loading Framework for On-Device Large Model Inference

SparKV is an intelligent KV cache loading framework for on-device large model inference. Its core lies in an adaptive KV cache loading strategy, combining cloud streaming and local computing. It reduces the first token time by 1.3-5.1x and energy consumption by 1.5-3.3x on edge devices, providing a practical solution for on-device large model deployment. The key is to balance computing and communication costs, dynamically select KV cache acquisition methods, while maintaining unchanged output quality.

On-device large model deployment faces bottlenecks in the Prefill phase: processing the complete input context requires computing a large amount of KV cache, which is time-consuming and memory-intensive in long-context scenarios, leading to latency and energy consumption issues. Traditional optimizations focus on model compression and operator optimization, with less attention to the KV cache aspect. However, there is great potential for KV cache optimization in hybrid deployment scenarios that combine cloud resources.

The core of SparKV is the adaptive KV cache loading strategy:

1. Hybrid Acquisition Strategy: Dynamically select local computing or cloud streaming for each KV block, balancing factors such as network conditions and device computing power;
2. Execution Path Overlap: Cloud transmission and local computing are parallelized to avoid resource idleness;
3. Cost Modeling: Model cloud transmission costs (data volume, bandwidth, stability) and local computing costs (model size, device computing power, power consumption), and combine runtime scheduling optimization to adapt to dynamic environments.

Experimental verification of SparKV's effects:

• First Token Time (TTFT): Reduced by 1.3-5.1x, improving interactive experience;
• Energy Consumption: Energy consumption per request reduced by 1.5-3.3x, extending battery life and reducing heat generation;
• Response Quality: KV cache equivalence ensures that the output accuracy is the same as the baseline solution, with no quality degradation.

SparKV is suitable for multiple scenarios:

• Smartphone Assistants: KV cache of historical conversations is obtained from the cloud, while new content is computed locally for fast response;
• Smart Home Devices: Offload more computing to the cloud to adapt to limited computing power;
• In-Vehicle AI Systems: Adaptive scheduling to handle unstable networks, ensuring availability and performance.

SparKV has limitations: it relies on cloud infrastructure, requires encryption to protect sensitive KV data during transmission, and multi-tenant scenarios need further research. Future directions include more fine-grained adaptive strategies, combination with model compression, and expansion to tasks such as image generation. Conclusion: SparKV optimizes KV cache through end-cloud collaboration, significantly improving on-device inference performance and providing key technical support for large model edge deployment.