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The race to build a distributed GPU runtime
Featured Posts Latest Posts Mike Beaumont For a decade, GPUs have delivered breathtaking data processing speedups. However, data is growing far beyond the capacity of a single GPU server.
When your work drifts beyond GPU local memory or VRAM (e.g., HBM and GDDR), hidden costs of inefficiencies show up: spilling to host, shuffling over networks, and idling accelerators. There’s a strong argument to be made that RAPIDS cuDF/RAPIDS libcudf drives NVIDIA’s CUDA-X Data Processing stack, from ETL (NVTabular) and SQL (BlazingSQL) to MLOps/security (Morpheus) and Spark acceleration (cuDF-Java). Theseus ships its own profiler/observability so teams can see compute, file I/O, memory tier occupancy, and network usage per stage, with low overhead, making it practical to tune data motion instead of guessing.
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