V-Rex: Real-Time Streaming Video LLM Acceleration via Dynamic KV Cache Retrieval

Streaming video large language models (LLMs) are increasingly used for real-time multimodal tasks such as video captioning, question answering, conversational agents, and augmented reality. However, these models face fundamental memory and computational challenges because their key-value (KV) caches grow substantially with continuous streaming video input. This process requires an iterative prefill stage, which is a unique feature of streaming video LLMs. Prior works reduce excessive cache overhead by utilizing the KV cache retrieval algorithm, which offloads the full KV cache to CPU memory or storage, then selectively fetches the most relevant entries. Nevertheless, due to its iterative prefill stage, they suffer from significant limitations, including extensive computation, substantial data transfer, and degradation in accuracy. Crucially, this issue is exacerbated for edge deployment, which is the primary target for these models. The memory footprint exceeds the memory capacity within minutes of video streams, making low-latency, energy-efficient inference infeasible.

In this work, we propose V-Rex, the first software-hardware co-designed accelerator that comprehensively addresses both algorithmic and hardware bottlenecks in streaming video LLM inference. At its core, V-Rex introduces ReSV, a training-free dynamic KV cache retrieval algorithm. ReSV exploits temporal and spatial similarity-based token clustering to reduce excessive KV cache memory across video frames, and dynamically adjusts token selection per transformer layer and attention head to minimize the number of selected tokens. To fully realize these algorithmic benefits, V-Rex offers a compact, low-latency hardware accelerator with a dynamic KV cache retrieval engine (DRE), featuring bit-level and early-exit based computing units, as well as hierarchical KV cache memory management. Evaluated on COIN benchmarks, V-Rex achieves unprecedented real-time of 3.9-8.3 FPS and energy-efficient streaming video LLM inference on edge deployment with negligible accuracy loss. While DRE only accounts for 2.2% power and 2.0% area, the system delivers 1.9-19.7× speedup and 3.1-18.5× energy efficiency improvements over AGX Orin GPU. This work is the first to comprehensively tackle KV cache retrieval across algorithm and hardware, enabling real-time streaming video LLM inference on resource-constrained edge devices, with clear potential for scalable deployment in large-scale server environments.