GPU INSTANCES

YOUR GPU.
YOUR MACHINE.
FULL CONTROL.

Dedicated GPU machines with root access via SSH. Pre-installed ML stack, your choice of hardware, no shared infrastructure. Run training, inference, or anything else — it's your box.

CONTACT SALES

RAW GPU ACCESS

FULL SSH ACCESS

Root access to a Linux machine with NVIDIA GPUs attached. Install anything, configure anything, run anything. No platform opinions, no restrictions.

PRE-INSTALLED ML STACK

CUDA, cuDNN, PyTorch, TensorFlow, JAX, Docker, and JupyterLab ready to go. Start training in minutes, not hours. Override anything you don't need.

CHOOSE YOUR GPU

H100, H200, B200, A100, and more. Pick the GPU that fits your workload and budget. Single-GPU or multi-GPU configurations on one machine.

DEDICATED HARDWARE

One customer per machine. No virtualization layer, no shared GPUs, no noisy neighbors. Physical isolation by default.

PERSISTENT STORAGE

NFS-mounted persistent filesystems survive instance restarts. Local NVMe for fast scratch space. Your data stays safe even if you terminate and re-provision.

MONTHLY COMMITMENTS

Minimum monthly contracts with predictable pricing. No surprise bills, no per-minute metering complexity. Reserve the machine for as long as your project needs it.

HOW IT WORKS

PICK YOUR HARDWARE

Tell us which GPU type, how many per machine, and your contract length. We'll confirm availability and pricing.

WE PROVISION

We set up a dedicated machine with your chosen GPU configuration, install the ML stack, mount persistent storage, and generate your SSH credentials.

SSH IN AND GO

We hand you an IP address and SSH key. Log in, run nvidia-smi to see your GPUs, and start working. Everything is pre-configured and ready.

BUILT FOR

ML DEVELOPMENT

Prototype, experiment, and iterate on a GPU machine you fully control. JupyterLab for notebooks, Docker for reproducibility, full root for custom environments.

SELF-HOSTED INFERENCE

Run your own vLLM, TGI, or custom inference server. Full control over configuration, scaling, and optimization. No managed inference abstractions in the way.

SINGLE-NODE TRAINING

Fine-tune models that fit on one machine. Multi-GPU configurations with NVLink for data parallelism. No cluster overhead for workloads that don't need it.

READY FOR DEDICATED GPU ACCESS?

Tell us what you're building and we'll match you with the right hardware.