GPU Cloud Orchestration

Deploy AI Workloads Across Any Cloud

GPU resources are scarce and scattered across providers. LayerOps federates GPUs from AWS, GCP, OVH, Scaleway and your own on-premise servers into a single orchestration layer. Auto-provision the right GPU, wherever it's available — no stock shortages, no vendor lock-in.

LayerOps GPU federation hub connecting AWS, GCP, OVH, Scaleway and on-premise GPU resources

Cross-Cloud GPU Federation

One control plane, every GPU

LayerOps orchestrates GPU resources from any cloud provider and your own bare-metal servers through a single control plane. Tag your GPU pools, define provider priorities, and let the platform automatically find available capacity. When one provider runs out of stock, workloads shift to the next — no manual intervention, no downtime.

See all supported providers →

Configuration comparison

Deploy an LLM inference service with GPU acceleration. The traditional approach requires cloud-specific CLIs, manual driver installation, and no built-in scaling. LayerOps handles it in a single service definition.

Traditional GPU Setup

# 1. Provision GPU instance (provider-specific)
aws ec2 run-instances \
  --instance-type p4d.24xlarge \
  --image-id ami-0abcdef1234567890 \
  --key-name my-key

# 2. Install NVIDIA drivers
sudo apt install nvidia-driver-535
sudo apt install nvidia-container-toolkit
sudo systemctl restart docker

# 3. Run model manually
docker run --gpus all \
  -p 11434:11434 \
  ollama/ollama serve

# 4. Load model
docker exec -it ollama \
  ollama pull llama3.1

# Still needed:
# - Load balancer (manual)
# - SSL certificates (manual)
# - Monitoring (Prometheus + nvidia_exporter)
# - Autoscaling (not available)
# - Failover (manual migration)

LayerOps

services:
  - id: llm-inference
    name: LLM Inference
    dockerConfiguration:
      image: ollama/ollama
      imageVersion: latest
    countMin: 1
    countMax: 1
    constraints:
      tags:
        - GPU
    useNvidiaGpu: true
    ports:
      - listeningPort: 11434
        protocol: tcp
        loadBalancerRules:
          - customDomains:
              - llm.example.com
            publicPort: 443

# GPU auto-provisioning, cross-cloud
# failover, SSL, monitoring, autoscaling
# — all included. Zero extra config.

See it in Action

A glimpse into the LayerOps console

Intuitive Dashboard

Deploy, monitor, and manage all your containers from a single console.

console.layerops.io

Click here

Try mock-up

Explore the console with sample data — no signup required

Why teams choose LayerOps for GPU workloads

GPU Auto-Provisioning: LayerOps automatically provisions GPU instances from the cheapest available provider. No manual capacity hunting, no cloud console hopping. Tag your instance pools with GPU, deploy your service — the platform handles the rest.
Cross-Cloud GPU: Span GPU workloads across AWS, GCP, OVH, Scaleway and on-premise infrastructure. Provider goes out of stock? Traffic shifts automatically to the next available pool — zero downtime, zero manual intervention.
Sovereign AI: Deploy LLMs on your own infrastructure. No tokens sent to external APIs, no data leaving your perimeter. Full sovereignty over your AI stack — compliant with defense, healthcare and finance regulations.

Ollama Marketplace: One-click deployment of Llama 3, Mistral, DeepSeek and other open models from the built-in Ollama marketplace. Select a model, pick your GPU pool, deploy — your private LLM is live in minutes.
MCP Server: Manage your GPU infrastructure through natural language via the Model Context Protocol. Let AI agents deploy services, scale instances, and troubleshoot your fleet — directly from any MCP-compatible assistant.
Cost Optimization: Real-time cost comparison across GPU providers. LayerOps scheduling places workloads where the price-performance ratio is best. Per-environment cost analytics help you track and optimize your AI infrastructure spend.

Traditional vs LayerOps

End GPU provisioning pain

Traditional GPU provisioning means managing cloud-specific CLIs, installing NVIDIA drivers manually, dealing with stock shortages from a single provider, and paying for idle resources. LayerOps automates the entire workflow — from GPU discovery to cross-cloud failover — in a single integrated platform.

Traditional manual GPU provisioning versus LayerOps cross-cloud GPU orchestration

Sovereign AI Infrastructure

Your models, your data, your rules

Deploy Llama 3, Mistral, DeepSeek or any open LLM on your own GPU servers — on-premise or in sovereign clouds. No tokens sent to OpenAI or Anthropic APIs. Your training data, model weights and inference results stay inside your perimeter. Compliance-ready for defense, healthcare, finance and any regulated industry.

Discover LayerOps On-Premise →

	DIY GPU Infrastructure	LayerOps
SetupTime to first GPU deployment		Under 10 minutes
Infrastructure as Code		Built-in, one-click or YAML
GPU driver management		Fully managed
Learning curve		Minimal — built for developers
GPU ManagementCross-cloud GPU scheduling		Native — any provider, single pane
GPU stock availability		Cross-cloud — always available
GPU auto-provisioning		Built-in, tag-based
On-premise GPU integration		Native — mix cloud + on-premise
AI / LLMLLM deployment (Ollama)		One-click Ollama marketplace
Model catalog		Built-in (Llama, Mistral, DeepSeek…)
Data sovereignty		Guaranteed — no external API calls
MCP Server integration		Built-in AI infrastructure management
OperationsCost analytics		Built-in per-provider breakdown
Autoscaling GPU workloads		Native, cross-provider

Explore related capabilities

LayerOps GPU orchestration integrates with the full platform. Deploy on your own infrastructure with LayerOps On-Premise. See all supported cloud providers. Build golden paths for your AI teams with Platform Engineering. Guaranteed reversibility — export anytime, zero lock-in. Already on Kubernetes? See LayerOps vs Kubernetes.

See it in action

Explore the LayerOps console without creating an account. Browse environments, GPU services, monitoring dashboards and deployment configs — all with realistic data.

Ready to deploy AI workloads?

Provision your first GPU service in minutes, not days.

GPU Cloud Orchestration — Deploy AI Workloads Across Any Cloud | LayerOps