GPU Optimizer
Smart GPU Orchestration for Maximum Resource Utilization
Software Defined GPUs
Exostellar solves GPU challenges of oversubscription, underutilization, elevated costs, and long queues for GPU access with Software-Defined GPUs (SDG), enabling real-time fractionalization and resource allocation, allowing multiple heterogeneous workloads to share a single GPU efficiently while optimizing performance and cost.
Here’s How To Make GPU Shortages Irrelevant
Today’s AI infrastructure faces significant challenges, including underutilized GPUs, inefficiencies from oversubscription, high costs due to overprovisioning, and delays caused by static resource management. Exostellar’s Software-Defined GPUs (SDG) address these issues by enabling real-time resource fractionalization and dynamic allocation. This allows multiple heterogeneous workloads to efficiently share a single GPU, maximizing utilization while cutting costs by over 50%. In the demo, you’ll see how Exostellar optimizes GPU performance with intelligent scheduling, seamless workload sharing, and scalable solutions that eliminate downtime and manual intervention, ensuring faster workflows and unparalleled cost efficiency.
Benefits
Maximize Utilization
Over 80% GPU efficiency by dynamically allocating compute & memory, eliminating idle resources.
Run More Workloads
Run multiple heterogeneous workloads per GPU optimizing usage while reducing hardware needs.
Lower Costs
Over 50% savings by eliminating over-provisioning and optimizing GPU allocation.
Use Cases
1. Utilize Idle GPUs During Interactive Development
Challenge: GPUs remain idle during scripting and debugging.
Exostellar: Dynamically reallocates unused GPU capacity, reducing wait times and improving efficiency.
2. Train & Fine-Tune Without Delay & Failures
Challenge: Training delays due to preprocessing bottlenecks and out-of-memory (OOM) failures.
Exostellar: Smart workload balancing and live migration prevent resource waste and ensure continuous model training.
3. Eliminate Wastage During Inference
Challenge: Running multiple small models separately leads to inefficient GPU usage.
Exostellar: Uses intelligent GPU slicing to consolidate workloads, maximizing performance and reducing overhead.
