Core Insights - The rapid development of AI large models has highlighted GPU memory capacity as a critical bottleneck for training and inference efficiency [1][3] - Fourth Paradigm has launched the "Virtual VRAM" plug-in virtual memory expansion card, which transforms physical memory into a dynamically scheduled memory buffer pool, allowing for elastic expansion of GPU computing resources [1][2] Company Overview - Fourth Paradigm's "Virtual VRAM" can expand the virtual memory capacity of a single graphics card up to 256GB, significantly enhancing the capabilities of existing GPUs without requiring hardware changes [2] - The product is designed for two main application scenarios: addressing insufficient memory for large model single-card operations and enabling multiple models to be deployed on the same GPU in light-load scenarios [2] Industry Implications - As the number and parameter scale of AI models continue to grow rapidly, memory capacity has become a key factor in building AI capabilities and controlling costs for enterprises [3] - The new product from Fourth Paradigm is expected to provide a cost-effective computing expansion solution, helping users maintain high performance while achieving cost reduction and efficiency improvement [3] - Future plans include collaborations with more memory manufacturers to further optimize and popularize AI infrastructure [3]