Spot & On-Demand Instances

STOP FIGHTINGINFRASTRUCTURE.

You are an ML Engineer, not a Kubernetes administrator. HypervizeTM completely abstracts away DevOps, driver mismatches, and container orchestration. First, secure your compute hardware—then dynamically inject your preferred ML environment right before launch. Get root access in under 60 seconds.

The Legacy Cloud
45+ Minutes
$ terraform apply -auto-approve
$ ssh ubuntu@ec2-instance...
$ sudo apt-get install -y nvidia-driver-535
E: Unmet dependencies. Try 'apt --fix-broken install'
$ docker pull pytorch/pytorch:2.3.0-cuda12.1-cudnn8-devel
Extracting [===================>] 14.2GB
Error: CUDA Out of Memory during init...
Pre-Provisioned
The HypervizeTM Way
45 Seconds
~ $ hf-cli launch --tier spot --type b200-1x --env pytorch
[==================================] 100%
[+] Allocating 1x NVIDIA B200 spot instance...
[+] Attaching optimized PyTorch 2.3 environment...
Ready. Connected to JupyterLab on port 8888.

BUILT FOR TIME TO RESULTS

Every second spent configuring drivers is a second not spent training. We optimize the entire hardware and software stack so you don't have to.

Dynamic Provisioning

Configure your stack at the exact moment of launch. After selecting your hardware capacity, we dynamically inject pre-warmed container images directly into the bare-metal host. PyTorch, vLLM, and DeepSpeed run instantly with zero setup.

Spot Pool Economics

Why pay on-demand prices for batch workloads? Our intelligent spot orchestration automatically provisions unutilized hardware globally, saving you up to 60% on B200 and H200 instances.

Frictionless Access

No complex IAM roles or VPC networking required. Your public SSH keys are automatically injected on boot. Securely forward Jupyter or VS Code Server ports to your local machine instantly.

LIVE HARDWARE INVENTORY

ALL INSTANCES INCLUDE BANDWIDTH & BASE OS, WITH CONFIGURABLE OPTIONS FOR ML FRAMEWORKS, STORAGE, AND MORE.

VIEW ENTERPRISE CONTRACTS →
Hardware Tier Specifications Network On-Demand Spot Price
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