AI Factories Should Focus on
Output.
Users want the product, not a deep dive into how it was made. And they want it at the best price possible. AI factories need to be efficient and self-managing, with all the moving parts taking care of themselves. Kubex delivers this by continuously monitoring workload behavior and demand patterns across your AI factory and acts to maximize throughput and drive down cost per workload.
Your GPU, CPU, and XPU infrastructure runs harder, schedules smarter, and scales to meet demand across AI Inference, Agentic, and Learning without manual intervention.
The Problem
AI services have a lot of moving parts.
Running an AI factory means operating expensive heterogeneous GPU, CPU, and XPU infrastructure at the scale your AI workloads demand. AI use cases and resulting infrastructure requirements drastically differ between Inference, Agentic, and Learning use cases. Collectively, demand becomes unpredictable, scheduling is imprecise, and utilization swings wildly between peaks and troughs.
The result is infrastructure that’s either overwhelmed or underused, and cost-per-workload numbers that are hard to defend.
Monitoring tells you what happened. Kubex changes what happens next by continuously optimizing scheduling, placement, and resource allocation across your entire infrastructure to maximize throughput and minimize cost per output.
SOUND FAMILIAR?
“We’ve literally given each AI Application entire GPUs because it’s too difficult to fractionalize and feel confident about performance and SLA.”
Kubex optimizes GPU and AI Infrastructure resource management. Kubex continuously optimizes scheduling, placement, and allocation across your infrastructure so throughput stays high and cost per output stays low, automatically.
How It Works
Continuous autonomous optimization, governed by your policies.
Kubex operates as a continuous control loop across your AI factory. It observes actual workload demand, infrastructure utilization, and scheduling efficiency, then acts within the boundaries you set to keep your GPU and AI infrastructure running at peak output without manual intervention.
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Analyze
Ingests real-time and historical metrics across all GPU pools, compute resources, and workloads to build performance and utilization models for every workload. Factors in GPU models in use, performance benchmarks and provider cost data to provide a complete view of demand patterns.
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Optimize
Calculates optimal sizing and sharing strategy for each AI workload, intelligently recommending time slicing strategies, MIG configurations or MPS based on the infrastructure in use (and/or available in the hosting environment). Effectively enables GPU bin packing, driving down infrastructure requirements.
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Automate
Applies changes autonomously within your defined policy guardrails to continuously adjust GPU allocations, CPU and Memory allocations, scheduling priorities, and autoscaling parameters. Seamlessly integrates with NVIDIA KAI scheduler to provide full closed loop automation of optimization actions.
Capabilities
What autonomous optimization covers.
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Precise GPU Request Sizing
Analyzes actual workload behavior to right-size GPU resource requests, eliminate over-allocation waste, and enable fractional GPU assignments so every sliver of GPU capacity is put to productive use.
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Intelligent AI Job Scheduling
Safely share a GPU across multiple containers by integrating with the KAI scheduler to automate fractional allocations in production, maximizing utilization without contention or compromise to workload performance.
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GPU Model Optimization
Select the best GPU model for each workload based on its specific compute requirements, and where supported, automatically determines the optimal MIG slice to maximize yield and efficiency on shared hardware.
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Predictive Node Scaling
Learns GPU workload patterns to proactively pre-warm nodes ahead of demand, eliminating startup latency, while continuously tuning autoscaler configurations, including scale-to-zero for idle GPU resources to match real demand rather than static guesses.
Results
What AI Factory teams achieve with Kubex.
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40-70%
Improvement in cluster-wide GPU utilization driving higher customer satisfaction
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Competitive Advantage
Innovative optimization and automation improves competitive profile
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+40%
Customer density growth improvements on existing infrastructure
Control & Governance
Autonomous doesn’t mean uncontrolled.
Human in the loop provision allows you to stay on top of automation and changes. Sensitive workloads can be placed in recommendation-only mode. Everything Kubex does is logged, auditable, and reversible.
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What you control
- Optimization scope by workload class, queue, or GPU pool
- Min/max resource bounds and scheduling priority tiers
- Throughput SLOs and cost-per-workload targets
- Change velocity — how aggressively Kubex acts
- Rollback triggers and automatic revert conditions
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What Kubex handles autonomously
- Continuous scheduling and placement optimization
- Cluster-wide throughput tuning and idle capacity reclamation
- Demand-driven autoscaling across job queues
- Cost attribution and efficiency tracking per workload
- Rollback if post-change throughput or cost metrics degrade
See your AI Factory running at full output.
See Kubex in action for yourself or talk to our team about your AI factory environment. Most teams have full infrastructure visibility and autonomous throughput optimization running within days of deployment.
