This research introduces a plug-and-play 1.x-bit KV cache quantization technique that significantly reduces memory requirements for video processing in large language models.

• Addresses the memory bottleneck caused by thousands of visual tokens from video frames
• Achieves nearly lossless performance while reducing memory requirements
• Enables processing of longer video sequences with existing hardware
• Offers a plug-and-play solution that can be integrated into existing VideoLLM architectures

For engineering teams, this breakthrough means more efficient deployment of video-capable LLMs on resource-constrained devices, faster inference times, and potential cost savings in cloud computing resources.

Plug-and-Play 1.x-Bit KV Cache Quantization for Video Large Language Models