US20260244562
2026-08-20
Physics
G06F12/023
The system and method focus on deploying large-scale language models with low latency by utilizing dynamic hierarchical storage and GPU optimization. It introduces a storage architecture divided into hot, warm, and cold tiers, specifically designed for large language models (LLMs). This setup significantly reduces the cold-start latency, making it imperceptible to human users. The system dynamically allocates GPU resources across numerous models based on real-time traffic, which reduces idle time and operational costs. It also integrates predictive preloading to ensure high-demand models are readily available in high-speed storage, enhancing responsiveness.
A core component is the dynamic hierarchical storage, which efficiently manages AI models based on their access frequency. High-demand models are stored in high-speed tiers, while less frequently accessed models are moved to persistent storage. The system employs a dynamic eviction mechanism that transfers models based on traffic patterns, ensuring optimal resource management without compromising performance. Additionally, predictive algorithms anticipate traffic surges, allowing models to be preemptively loaded, thereby reducing cold-start latency.
The system optimizes GPU utilization by combining serverless principles with traffic-based caching. This approach allows multiple AI models to share GPU resources, eliminating the need for dedicated GPUs for each model. Through intelligent scheduling and resource allocation, the system maximizes GPU utilization across diverse workloads. This results in cost-efficient AI model hosting, particularly beneficial for serverless environments and large-scale enterprise applications.
Scalability is achieved through dynamic caching and GPU sharing, tailored specifically for AI workloads. The system's architecture supports the management of thousands of concurrent models, ensuring efficient resource utilization. By dynamically scaling storage and computing resources based on traffic patterns, the system reduces the need for always-on infrastructure, thus significantly lowering operational costs. This makes it suitable for AI marketplaces and domain-specific LLMs across various industries.
To maintain high performance and availability, the system balances resource efficiency with consistent model accessibility. It leverages traffic prediction algorithms to preemptively manage storage and compute resources, enhancing responsiveness during fluctuating traffic conditions. The system dynamically caches frequently accessed models in high-speed storage, ensuring minimal loading time during traffic spikes. This approach significantly reduces cold start latency, improving user experience in real-time inference scenarios.