Up to 50% more effective capacity from the AI infrastructure you already own.
CORNDEX turns GPUs into a high-performance execution layer for the data-intensive workloads behind AI and advanced analytics.
Modern AI runs on massively parallel GPUs, fed by data pipelines designed for the CPU era. Every stage of a pipeline crosses between CPU and GPU, and the GPU waits at every crossing. The meter runs either way.
Effective Compute Utilization (ECU): the share of a paid GPU hour actually spent computing, from GPU-busy telemetry (an operational proxy). Published numbers range from 5% to below 70% at peak.
Microsoft Research classified 706 root causes across 400 production jobs that ran below 50% GPU utilization.
Nearly two thirds of the causes are data in motion, not computation.
Microsoft Research, ICSE 2024: 400 production deep learning jobs below 50% GPU utilization, 706 classified root causes, 85% of them fixable.
CORNDEX is a data engine that runs inside the GPU: scan, SQL, Python and machine learning as one pipeline, in one process. Data crosses from CPU to GPU once.
Six road trips per pipeline, and the GPU waits on every one of them.
One trip in. One answer out. The GPU never stands idle.
Scan, SQL, Python and ML run as a single pipeline. No hand-offs between separate systems, no glue code between engines.
The optimizer, the runtime and the storage path were designed for the GPU, not adapted to it.
SQL and Python, Parquet and Iceberg. Teams keep their tools and their data formats.
On-prem, private cloud, and air-gapped environments.
High Bandwidth Memory is the memory inside every AI GPU. The market is sold out and every unit is pre-sold: yield is not a quality metric, it is revenue. When yield drops, engineers investigate the production test data to find the cause.
A question joined the overnight queue. The answer came back the next day.
The same investigations run 5-10x faster. Engineers drill through billions of records live.
Every multiple of speed is a shorter loop from detection to fix. That is the metric the fab runs on.
Every memory generation stacks higher and starts below the maturity of the one before it. The yield climb restarts each time, so the investigation loop is a standing requirement, not a project.
Wherever a GPU works on data, the engine underneath is CORNDEX. CPUs orchestrate. GPUs execute.