Data-intensive GPU applications increasingly rely on NVMe SSDs to extend GPU memory. NVIDIA GPU Direct Storage (GDS) enables direct SSD-GPU transfers via PCIe peer-to-peer DMA, eliminating host staging. However, GDS retains three key limitations: (1) its control path traverses the kernel for every I/O request, introducing software overhead; (2) its stream-ordered path imposes strict total ordering even when workloads only require batch-level ordering; and (3) its closed-source user-space engine hinders diagnosis and custom scheduling. We present uGDS, https://github.com/ScaleX-IO/uGDS, an open-source user-space GPU Direct Storage library built on four principles: user-space control, unified cross-vendor abstraction, workload-aware scheduling, and end-to-end transparency. The host CPU manages the entire NVMe command lifecycle in user space, while payloads move via PCIe P2P DMA without consuming GPU compute. A minimal kernel module is used only for one-time privileged setup. uGDS partitions hardware queues between synchronous and batch traffic, enforces DMA-safe buffer lifetimes, and provides cuFile-compatible synchronous, batched, and stream-ordered APIs with polling or interrupt completion on both NVIDIA CUDA and AMD HIP. Evaluated on an A100 with a Samsung 990 PRO, uGDS achieves up to 12.12x GDS's batch-read bandwidth and 30.69x its batch-write bandwidth (8- and 4-KiB requests, batch size 128), and reduces 4-KiB synchronous write latency by 84.1% with two threads. Its stream-ordered path sustains 5.49 GiB/s on a single stream, where GDS retains only 40.8% of its synchronous bandwidth. In LMCache, uGDS reduces median time-to-first-token (TTFT) by up to 62.7% and raises serving goodput by 2.63x. On AMD, uGDS achieves up to 1.19x hipFile's synchronous-write bandwidth.

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