The AI infrastructure stack has five layers — and there’s a critical gap between two of them.
Agentic AI, Post-Training, Fine-Tuning
PyTorch, vLLM, Inference Engines
Ray, Distributed Training & Serving
Kubernetes, Container Scheduling
Bare-Metal GPU Clusters
Workloads like agentic AI and post-training are exploding. They run on PyTorch and inference engines like vLLM. Those frameworks rely on distributed compute — increasingly Ray — which sits on top of Kubernetes and bare-metal GPU clusters.
The stack is clear. The gap is not.
But here’s the problem: Kubernetes doesn’t natively understand GPUs the way AI workloads need it to.
K8s can schedule containers. It can’t intelligently manage multi-tenant GPU allocation, handle fractional GPU sharing, or optimize cluster utilization across training and inference jobs competing for the same hardware.
Kueue and Volcano are early attempts, but they’re not production-complete for complex AI workloads.
That’s the missing layer — and that’s where AutoScale.AI operates.
We sit between the orchestration and distributed compute tiers. Our PySpark-based control plane manages GPU cluster lifecycle — scheduling, allocation, and optimization — so platform teams don’t have to build custom operators from scratch.
We bridge Spark’s data pipeline strengths with Kubernetes-native orchestration for GPU-aware workload management.
GPU availability is the bottleneck. We make sure every GPU-hour counts.