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AI Infrastructure

GPU Compute for AI That Ships

ML infrastructure, model training, inference hosting, and LLM deployment. Managed by engineers who understand AI workloads—not just servers.

AI Infrastructure, Not Just Servers

Cloud GPU providers give you hardware. We give you a managed AI platform.

Latest NVIDIA GPUs

H200, H100, A100, L40S available with competitive pricing through our infrastructure partnerships.

Pre-Configured Environments

PyTorch, TensorFlow, CUDA, and popular serving frameworks ready to go. No days spent on setup.

ML-Native Support

Engineers who understand training runs, inference latency, and GPU utilization—not just generic server support.

Predictable Pricing

Monthly GPU costs you can budget for. No surprise per-token charges or variable cloud bills.

Who We Work With

ML Teams Scaling Up

Outgrowing single-GPU experiments? We build multi-GPU clusters that let your team train larger models without managing infrastructure.

CTOs Evaluating Options

Build vs. rent? Cloud vs. dedicated? We help you understand the tradeoffs and build infrastructure that makes sense for your scale.

Production AI Teams

Running inference for real users? We optimize for latency, throughput, and cost—the metrics that matter in production.

Self-Hosted LLM Users

Done paying per token? We deploy your models on dedicated GPUs with predictable monthly costs and complete data privacy.

ZenoCloud vs. Cloud GPU Providers

Cloud GPU (Lambda, CoreWeave) ZenoCloud Managed
Setup Self-service, you configure everything We build and configure for you
Environment Base OS, install your own stack Pre-configured ML environments
Support Generic infrastructure support ML-native engineers
Optimization DIY performance tuning We optimize for your workload
Monitoring Basic metrics GPU utilization, training metrics, alerts
On-Call Your problem 24/7 infrastructure monitoring
Let's Talk AI Infrastructure

Tell Us About Your Workload

Training models? Running inference? Hosting LLMs? We'll help you figure out the right infrastructure—and then actually build it.