InferGrid
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Infrastructure / Runtime flexibility

Your infrastructure. One operational layer.

Standardize how teams run inference across Kubernetes, cloud GPUs, and private infrastructure.

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The technologies below are architecture and compatibility targets described in InferGrid’s product context. Discuss your stack with the team to confirm supported configurations; these do not represent verified integrations or partnerships.

ARCHITECTURE & COMPATIBILITY TARGETS

KubernetesNVIDIADockerPyTorchOpenTelemetry

Technologies referenced in the product architecture. Confirm supported configurations with the team; no partnership is implied.

01 / Infrastructure

Build around accelerated compute.

InferGrid is designed as an operational layer above GPU infrastructure. Cloud Kubernetes, dedicated GPU clusters, and private environments are relevant deployment patterns.

  • Kubernetes clusters
  • Cloud GPU instances
  • Private & hybrid infrastructure
  • NVIDIA GPUs
Discuss your requirements

02 / Serving runtimes

Keep the runtime close to the model.

Containerized model serving creates a consistent foundation across different workloads. These runtimes are relevant compatibility targets.

  • vLLM
  • NVIDIA Triton Inference Server
  • NVIDIA TensorRT-LLM
  • Hugging Face TGI
  • PyTorch serving stacks
  • Custom containers
Discuss your requirements

03 / Telemetry

Connect the operational signals.

Inference operations require visibility across requests, models, and hardware. The context identifies these technologies and patterns for observability.

  • Prometheus
  • Grafana
  • OpenTelemetry
  • DCGM / GPU metrics
  • Logs & traces
Discuss your requirements

04 / Cloud environments

Retain infrastructure flexibility.

The operational model is intended for teams working across accelerated cloud and Kubernetes environments. Validate your cloud and deployment requirements during a technical meeting.

  • AWS
  • Microsoft Azure
  • Google Cloud
  • Dedicated infrastructure
Discuss your requirements

The next step

Your models are ready.
Your infrastructure should be too.

Standardize how your team deploys and operates AI inference.

Book a meeting