Automated fractional GPUs
Run more workloads on every GPU
Automatically optimize GPU allocation and workload placement as AI workload demand changes. Run more workloads on existing GPUs within your performance policies and guardrails.
Trusted by leading teams worldwide
Capabilities
Increase GPU utilization with real-time automation
Optimize GPU allocations
in real time
Automatically adjust GPU memory allocation, compute share, and sharing mode as AI workload requirements change.
Optimize workload placement
and density
Determine where workloads should run and how many can share each GPU based on their performance requirements, reducing stranded capacity while maintaining stability.
Works with your existing GPU setup
Zesty works with NVIDIA’s CUDA-native GPU fractioning, time-slicing, MPS, and Kubernetes schedulers such as KAI Scheduler. No application code changes, model changes, or manual edits to Kubernetes manifests are required.
Comparison
From unused capacity to more workloads per GPU
Benefits
Get more from the GPUs you already have
Improve GPU utilization
Continuously optimize memory and compute allocation to run more workloads on the GPU you already have.
Minimize additional GPU spend
Support growing AI demand with existing infrastructure and add new GPUs only when you truly need them.
Protect workload performance
Keep optimization aligned with each workload’s latency, resource, and availability requirements as usage grows and demand shifts.
More Capabilities
Optimize at every layer
PV autoscaling
Automatically rightsize persistent volumes based on usage to reduce costs and maintain availability.
Azure Commitment Optimization
Continuously align commitments with usage to maximize coverage and minimize lock-in risk.
FastScaler
Reduce app startup time by up to X5 to absorb spikes without throttling or OOMs.
Adaptive pod placement
Reposition unevictable pods to reduce fragmentation and enable node consolidation.
Multi-dimensional Autoscaling
Optimize pod resources and replica counts to maximize performance and cost efficiency.
AWS Commitment Optimization
Continuously align commitments with usage to maximize coverage and minimize lock-in risk.
Compute cost visibility
Analyze workload costs, identify waste, and automate with clear data-driven insights.
Java Resource Management
Rightsize Java heap, pod CPU, and memory in real time to cut cloud costs without compromising performance.
