Trickums: The VRAM Illusion System of ForgeBorn

Trickums is a GPU memory virtualization system designed to extend the effective VRAM capacity available to AI workloads through a multi-tiered architecture spanning local GPU memory, system RAM, NVMe storage, and distributed remote nodes. By intercepting CUDA and framework-level allocation calls, Trickums presents a unified, oversized memory space while dynamically paging data between tiers based on access patterns and execution context. The system implements demand-driven page migration, eviction policies, and predictive prefetching, enabling large models to execute on hardware with limited native VRAM. Integration with orchestration layers allows coordinated scheduling of model shards and data movement, reducing stall conditions and maximizing resource utilization across heterogeneous environments. This document describes the architecture, memory management strategies, and system design of Trickums, including comparisons to unified memory systems, GPU-direct data paths, and remote memory frameworks. It outlines how VRAM oversubscription can be achieved transparently while maintaining correctness, performance constraints, and scalability in distributed AI compute networks.