WhatA database engine that keeps hot data resident in GPU VRAM and uses GPU compute for sorting, joining, and querying — treating the GPU as a combined storage+compute unit rather than just an accelerator.
SignalCommenters independently arrived at the realization that GPUs have both massive memory and massive compute sitting idle, and that a database format designed for GPU-resident data could exploit both simultaneously for operations like sorting and compaction.
Why NowConsumer and datacenter GPUs now have 24-80GB of high-bandwidth VRAM, enough to hold meaningful working datasets, and CUDA/compute APIs have matured to support complex data operations beyond ML inference.
MarketReal-time analytics, financial trading systems, and ad-tech companies paying $50K+/year for fast query engines; TAM overlaps with the $80B database market; competitors like HeavyDB exist but haven't achieved mainstream adoption.
MoatPurpose-built query optimizer and storage format for GPU memory hierarchies would be extremely hard to replicate; performance benchmarks create strong switching costs once production workloads depend on it.
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