Unlock Full Value of your GPUs with Intelligent Scheduling
Your expensive GPUs sit idle because traditional schedulers treat them like generic compute.
Radiant's Intelligent Scheduler changes that by placing workloads with surgical precision and reducing compute fragmentation.
With GPU Clusters, you can build bigger, faster, better - for less
Five GPU-Aware Capabilities That Deliver Peak Efficiency
Every component of the Intelligent Scheduler is purpose-built for the realities of modern AI infrastructure.
Automated Failover
GPU failures aren't edge cases, they're a daily reality. Xid errors, dropped packets, power loss: any of these can derail your workloads within minutes. Radiant's control plane integrates failover directly into the scheduler, instantly cordoning failed nodes, draining workloads, and rescheduling onto healthy hardware, ensuring high availability for your workloads.
Fractional Sharing
Multiple tenancy modes help you deploy both public and private AI clouds, and cover a large addressable market.
Programmatic lifecycle management
Integrated within the platform with bare-metal level performance. No off-platform isolation andno performance tax.
Topology Aware and Node level Bin-Packing
Integrated within the platform with bare-metal level performance. No off-platform isolation andno performance tax.
Suspend & Resume Compute Effortlessly
Integrated within the platform with bare-metal level performance. No off-platform isolation andno performance tax.
One Cluster, Every Workload
AI/ML workloads vary widely, from large-scale training jobs that consume entire GPUs, to low-latency inference services that need many small, elastic slices. Traditionally, organizations build separate clusters for each workload type, introducing silos, higher costs, and more operational overhead. With a purpose-built, GPU-aware scheduler, Radiant runs the full spectrum of workloads on a single unified cluster, dynamically repurposing hardware throughout the day and night.
Multi-node Distributed Training
Large training jobs placed on dedicated, full-GPU resources.
Bare Metal Provisioning
Full bare-metal supercomputing without virtualization overhead.
MIG-Sliced Inference
Inference pods using just 1/7th of an NVIDIA GPU run alongside six others on the same device.
Faster Reconfiguration
Switch between training and inference or from bare-metal to virtual machines.
