The intelligent orchestration layer for heterogeneous AI infrastructure. Eliminate idle waste and scale your research with one unified control plane — on-prem first, cloud only when it pays.
From individual research nodes to global enterprise clusters. Three deployment models, one operating model — start small and expand without rewrites.
Unlock 95% utilization on existing local hardware with 1% block partitioning and MIG slicing. Ideal for university AI labs and small AI teams of 4 to 32 GPUs.
Consolidate siloed GPU pools across campuses or business units into a unified virtual resource with fair-share scheduling and zero-downtime live migration.
Maximize local utilization first, then auto-burst to AWS / GCP / Azure / Naver / KT Cloud only when local capacity is full. Spend cloud money on demand only.
Native hardware optimization, intelligent orchestration, and zero-downtime workload mobility — built on Kubernetes and the NVIDIA device plugin.
Granular CUDA core and VRAM slicing in 1% blocks. Run 100 simultaneous tenants per H100 with hard isolation, far below the per-MIG-slice minimum.
Real-time deep-learning execution detection, deadline + priority queues, and proactive memory control prevent OOM and starvation across thousands of jobs.
Zero-checkpoint workload hand-off between local and cloud nodes. Reclaim idle GPUs in seconds instead of waiting for natural job completion.
How research labs, growth-stage AI companies, and enterprise platform teams accelerate their AI development with CNLab.
“GPU utilization improved from 5% to over 95% in one semester. We added zero hardware and tripled our throughput.”
“Cloud costs reduced by 70% while doubling our training capacity. Burst-out is fully transparent — our researchers never see the boundary.”
“Zero resource conflicts and sub-second job starts for 100+ developers. The scheduler does in real-time what we used to do in spreadsheets.”
Comprehensive guides, deep technical documentation, and runnable examples to optimize your stack from day one.
Get your first cluster up and running in under 5 minutes with our automated CLI agent. Provision a GPU profile, attach storage, and open JupyterLab without leaving the terminal.
Detailed whitepapers on GPU slicing, hybrid cloud routing logic, and security compliance — written for engineers, reviewed by infrastructure leads.
Browse documentation →# Provision a sliced H100, 25% block $ cnlab server create \ --image pytorch:2.4-cuda12.4 \ --profile h100-25pct \ --storage 50gb \ --idle 4h ✓ Server provisioning... ETA 18s ✓ Ready: https://nb.cnlab.ai/srv_01HX...
Optimizing the world's GPU resources for the AI era — with intelligent orchestration, not more hardware.
CNLab was founded to solve the fundamental inefficiency in AI infrastructure. We believe that intelligence should not be gatekept by expensive hardware, but maximized through intelligent orchestration. Our platform bridges the gap between idle silicon and breakthrough research.
We are a team of cloud-native engineers, GPU schedulers, and infrastructure researchers — based in Seoul with deployments across Korea, the EU, and North America. Every line of CNLab is built to make GPUs productive for somebody, somewhere, right now.
Learn about our vision →Deployments across 20+ countries and major research institutions, including five of the top ten Korean universities and three Fortune-500 enterprises.
Speak with a solutions architect today for a free technical consultation and cluster performance audit. We'll model your savings against real cloud spend in your inbox within one business day.