How research labs, growth-stage AI companies, and enterprise platform teams accelerate their AI development with CNLab. Filterable by industry and architecture.
A 30-person AI startup cut their cloud spend by 70% in three months by treating their on-prem A100s as the primary pool and AWS as overflow.
A leading Korean university federated three GPU rooms — one with H100s for training, one with A100s for evaluation, and an L40S inference farm — under a single scheduler.
A Fortune-500 industrial AI team gave 100+ developers self-service GPU access in two weeks, replacing a 14-week manual ticket process.
A graduate-level deep-learning course at a top Korean university tripled student-throughput on the same cluster by moving to 1% Block partitioning.
A government research lab consolidated four siloed compute facilities — two H100, one MI300, one L40S — under one CNLab control plane with full audit.
A computer-vision startup retired a 4,000-row "GPU-allocation" spreadsheet and replaced it with role-based quotas and self-service provisioning.
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