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Products
GPU compute, pre-built environments, and dedicated VMs on a distributed network built for resilience. Deploy anything from inference endpoints to CI/CD pipelines.
GPU Compute
Access GPUs on demand across our distributed network. Deploy inference and training workloads with automatic failover — when nodes fail, your workloads don't.
Pre-Built Environments
Deploy pre-configured VMs built for specific workflows — CI/CD runners, AI agents, and more. Every environment ships with all dependencies pre-installed, ready in minutes.
jobs:
train:
runs-on: [self-hosted, gpu, vectorlay]
steps:
- run: nvidia-smi
- run: pytest tests/Dedicated Virtual Machines
Spin up GPU-accelerated VMs with full root access. Bring your own container image or start from a base Ubuntu VM with NVIDIA drivers and Docker pre-installed.
$ nvidia-smi --query-gpu=name,memory.total --format=csv,noheader NVIDIA H100 80GB HBM3, 81559 MiB $ docker run --gpus all my-model:latest [ ready ] serving on :8000
Pricing
Pay only for what you run. Spin up in seconds, scale on demand, no contracts.
Environments
Pre-configured VMs for specific jobs — boot one and start working, no provisioning.
Self-hosted runners with GPU access. No queue times, unlimited build minutes.
More environments are landing continuously — AI agents, batch renderers, and notebook servers. Need a specific stack?
Request an environmentContribute your GPUs to the VectorLay network and earn. We handle routing, failover, and billing.