GPU Orchestration

GPU Orchestration is the heart of MemVerge.ai software and any human-centered enterprise AI factory. 

Supporting both Nvidia and AMD GPUs, the software provides enterprises with the ability to keep a watchful eye on their precious GPU resources, share them so the scarce GPU cycles are not wasted, and intelligently schedule access based on the priority of the projects.

In summary, utilization of scarce GPU resources is maximized with GPU orchestration. The results are lower computing costs, more AI workloads are supported, and complex optimization is available to all users because it’s automated.

GPU Orchestration Dashboard

Creating Fractional GPUs

Supports:

Use Cases

Maximize AI Workload Throughput and Cost Efficiency with GPU Fractionalization

Intelligent GPU sharing algorithms eliminate idle resources and maximize utilization.

Use Case

Integrated GPU Resource Management for Efficient, Productive Enterprise AI

Advanced scheduling policies and optimization of NVIDIA hardware ensures superior job performance.

Use Case

Technology

The following are descriptions of technologies under the hood of MemVerge.ai GPU Orchestration.

Real Time Monitoring & Optimization

Real-time GPU surfing (optimization) based on real-time monitoring, the needs of workloads, and relative priorities of the workloads.

Multi-Instance GPU (MIG) & Timeslicing

Multi-Instance GPU (MIG) partitions a GPU into isolated instances with dedicated resources, enabling parallel, secure workloads. Time-slicing allows workloads to get access to all GPU resources temporarily by switching between them rapidly. Together, MIG and time-slicing improve GPU utilization, support multi-tenant environments, and ensure efficient, predictable performance across diverse AI and compute workloads.

Department Billing

Tracks costed usage of GPU resources by department and projects. Allows a department to borrow resources and pay back the cost.

Spot Market Creation

Creating an internal spot market for GPU resources enables dynamic allocation based on real-time demand and job priority. Idle or underutilized GPUs are offered to lower-priority tasks at reduced cost, maximizing utilization. This market-driven approach increases efficiency, reduces waste, and aligns compute access with organizational value and urgency.

Priority Queueing

Priority queuing of AI jobs ensures that high-importance tasks are executed before lower-priority ones. By assigning priorities, GPU resources can be dynamically allocated to meet business or user needs. Higher priority projects interrupt lower priority projects. This optimizes performance, reduces wait times for critical jobs, and enhances overall efficiency in multi-user or multi-project AI environments.

Reservation & Bursting

Allows a project in need of GPU, to use (burst into) resources beyond what is reserved, by borrowing another department if resources are available.

GPU-centric Job Scheduler

Intelligently manages and allocates GPU resources to queued workloads based on factors like priority, availability, and resource requirements. It ensures efficient utilization, minimizes idle time, and balances performance across users and tasks, making it essential for high-demand environments like AI training, inference, and scientific computing.

Batch Job Scheduler

Manages the execution of queued, non-interactive tasks by organizing them into batches and assigning them to available compute resources. It optimizes job throughput, enforces priorities, and ensures efficient use of infrastructure, making it ideal for handling large-scale AI training, data processing, and simulation workloads.

Transparent Checkpointing

MemVerge.ai Transparent Checkpointing can be implemented stand-alone, or as a foundation for MemVerge.ai GPU Orchestration. The ability to suspend and resume jobs is what makes it possible to surf GPUs, burst into GPU resources unused by other projects, and maintain nodes if a GPU fails. Test drive the Checkpoint Operator for Kubernetes.

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