GPU infrastructure.
Seconds, not weeks.
Stop waiting for hardware. Stop juggling clouds. Unified orchestration for on-prem, cloud, and Lyceum GPUs. One control plane for your entire fleet.
The infrastructure bottleneck is real
Your ML teams need GPUs. But procurement takes months, cloud accounts are siloed, and existing clusters sit half-empty while queues grow.
Procurement takes forever
Weeks to get cloud approval. Months for on-prem hardware. ML projects stall waiting for compute.
Fragmented infrastructure
On-prem clusters, AWS, GCP, Azure. Different tools, different queues, no unified view.
Low utilisation
Expensive GPUs sitting at 40% utilisation. Capacity hoarding. No visibility into actual usage.
Get GPUs in seconds, not weeks
Self-service provisioning from CLI or API. No tickets, no approvals, no waiting. Your ML teams get compute when they need it.
One command to launch
VMs spin up in under 30 seconds. Pre-configured with CUDA, drivers, and your choice of ML framework.
Burst when you need it
On-prem full? Jobs automatically overflow to Lyceum Cloud. Seamless scaling, same API.
Keep your policies
Quotas, budgets, and access controls. Give teams autonomy without losing governance.
8× NVIDIA H100 80GB | InfiniBand 400Gb/s
GPU Utilisation
+47% improvementDouble your effective capacity
Most GPU clusters run at 30-50% utilisation. Lyceum's orchestration layer pushes that to 80%+ by eliminating idle time, optimising scheduling, and enabling preemption.
Smart scheduling
Jobs get matched to optimal hardware automatically. Gang scheduling for distributed training. Bin-packing for small jobs.
Preemptible workloads
Low-priority jobs run on spare capacity. High-priority work preempts when needed. No idle GPUs.
Real-time visibility
See exactly what's running, who's using what, and where the bottlenecks are. Data-driven capacity planning.
Complete infrastructure stack
Three products that work together to solve your GPU infrastructure challenges.
Orchestration
Unified control plane for all your GPU resources. On-prem, cloud, and Lyceum in one view.
- Single API across all providers
- Smart job scheduling
- Per-team cost attribution
Large-Scale Clusters
Dedicated GPU clusters from 8 to 8,000 GPUs. InfiniBand networking, engineering support.
- 400Gb/s InfiniBand
- H100, H200, GB200 available
- Multi-month reservations
Virtual Machines
On-demand GPU VMs for development, fine-tuning, and research. Launch in seconds.
- Self-service provisioning
- Per-second billing
- 1-8 GPUs per VM
Ready to modernise your GPU infrastructure?
Talk to our engineering team about your infrastructure needs.