CUSTOMERS / FIELD NOTES
Explore fictional customer stories across AI labs, enterprise AI, inference and media production. Each story shows a challenge, deployment shape and illustrative result.
01 / WORKLOADS
Different jobs. Different compute plans.
Browse deployment patterns as you replace these examples with your own customer proof.
AI LABS
GENERATIVE AI
ENTERPRISE AI
INFERENCE
MEDIA / RENDERING
02 / CASE STUDIES
A closer look at the cluster.
These fictional entries are CMS-managed and ready to replace with verified customer outcomes.
VANTA LABS
Generative AI
Training capacity without a reservation queue
Scaling multi-node model training had become difficult when cloud reservations took weeks to confirm.
RESULT / DEMO
An eight-node cluster was provisioned in one afternoon for a controlled training run.
3.4× faster provisioning
31% lower compute cost
8-node cluster
Read customer story ↗
KINETIC SYSTEMS
Enterprise AI
Private capacity for production inference
A product team needed predictable GPU capacity and network isolation for internal AI services.
RESULT / DEMO
Dedicated capacity and VPC controls enabled a staged migration from shared instances.
99.99% uptime target
6 services migrated
2 regions
Read customer story ↗
FRAMEWORK STUDIO
Media / Rendering
Render pipelines built around available GPUs
Rendering spikes made fixed on-premises capacity expensive and slowed peak production periods.
RESULT / DEMO
L40S capacity gave artists a repeatable burst workflow near their asset storage.
42% faster queues
120 jobs / day
L40S clusters
Read customer story ↗
Customer names, use cases and reported metrics are fictional template demonstration content.
03 / WHAT TO PUBLISH
Turn a story into useful buyer evidence.
A credible production case study connects the initial constraint with deployed capacity and a measured result.
01
Constraint
Document the workload, timeline and compute bottleneck.
02
Deployment
Show GPU count, region, network and storage configuration.
03
Measured result
Use customer-approved figures with a clear baseline and period.
CAPACITY / ON DEMAND
Put your workload on the map.
Share your target capacity and deployment plan with the infrastructure team.