AI factory
AI factory: from training to inference in production
An AI factory is more than GPUs. It is the stack that takes models from training through serving with isolation, storage and Day-2 ops.
Written by
YAPIO
Published on
Jul 12, 2026
Layers of an AI factory
Industry designs describe five layers: bare-metal GPU provisioning, an AI-ready Kubernetes (or Slurm) control plane, tenant isolation, certified AI tooling (Ray, Jupyter, job schedulers), and Day-2 operations, monitoring, billing, health checks.
Skipping layers creates fragile platforms: GPUs without fabric, Kubernetes without GPU operators, or training clusters without durable parallel storage for checkpoints.
Training and inference are different jobs
Training wants exclusive multi-GPU jobs, gang scheduling and high bisection bandwidth. Inference wants latency SLOs, autoscaling and often denser packing. Many factories run both paradigms: Slurm for batch training, Kubernetes for continuous serving, sometimes unified via operators.
Storage must feed both: high throughput for datasets and checkpoints, plus low-latency access for model weights at serve time.
How YAPIO approaches it
We start from workload reality, not a product catalog. Then we map layers, choose Slurm, Kubernetes or hybrid, and deploy with monitoring and runbooks. Product engineering and AI implementation sit beside the factory so the capacity is actually used.