High-performance storage engine that removes I/O bottlenecks slowing down LLM inference and GPU training.
The short version: a legitimate infrastructure play addressing a real cost center (GPU idle time from storage bottlenecks) for teams running LLMs at real scale — but it's squarely enterprise-infra, with no public pricing and real hardware prerequisites. Against running vLLM or Ray with generic storage, OpenLake's pitch is that a purpose-built KV-cache and storage layer measurably improves GPU utilization — worth real money at scale, since GPU time is the dominant cost. The catch: RDMA/GPUDirect benefits assume you already have that hardware, the framework integrations currently center on the …
Organizations running large-scale LLM inference and GPU training
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