NTSM: OS-Level Memory Middleware for Scaling AI Without Boundaries

The rapid scaling of large language models—from hundreds of billions to trillions of parameters—has rendered distributed memory management the critical bottleneck in modern AI systems. Existing frameworks manage cross-node memory entirely in user space, incurring serialization overhead that consumes up to 76.7% of data transfer latency, memory amplification reaching up to 6.7× from intermediate buffers, and escalating coordination costs that compound with cluster size. These overheads are fundamental: commodity operating systems lack abstractions for TB-scale tensors, cross-node sharing, and the sequential, layer-granular access patterns characteristic of transformer workloads. We present NTSM, a kernel-space middleware that recasts distributed memory management as a first-class OS primitive. Implemented as a Linux kernel module with a minimal kernel modification (one exported symbol on x86), NTSM exposes a symmetric virtual address space across cluster nodes where applications access remote data through ordinary memory operations. Page faults trigger zero-copy RDMA transfers with automatic coherence enforcement via a directory-based M/S/I protocol operating at 32KB–2MB Virtual Memory Region granularity—matching the natural access grain of transformer layers while amortizing fault overhead. By executing in kernel space, NTSM directly manipulates page tables, pins memory for DMA, and manages TLB state, eliminating the serialization, buffer copies, and user-kernel transitions inherent to user-space approaches. NTSM integrates transparently with existing frameworks: 152 lines modified for PyTorch FSDP, 89 lines for Ray Parameter Server. Evaluation on a 34-node RDMA cluster with models up to 175B parameters demonstrates up to 6.5× speedup in distributed training memory operations, 4.4× reduction in time-to-first-token for inference, and 47% reduction in parameter server iteration time—establishing kernel-level memory management as a viable foundation for next-generation distributed AI infrastructure.