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Available long-term
Rent by the quarter with a fixed bill and agreed runtime — built for workloads that keep running.
Cloud GPU servers
Run long jobswithout buying GPUs
GPU servers rented by the quarter, environments preconfigured. Built for model training, inference, medical imaging, and scientific computing.
Quarterly minimumEnvironment setup includedEngineers on call
Why rent long-term
Built for training, inference, and research projects that run for months at a time.
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Environment set up
CUDA, Python, Conda, PyTorch, TensorFlow, and Jupyter configured for your stack.
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Help when things break
Driver, dependency, or runtime trouble — engineers are on call to sort it out.
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No hardware to buy
Skip the upfront GPU and server spend, and the hardware maintenance that comes with it.
Long-term plans
Start from your model, VRAM needs, and timeline — then choose the server.
GPU Starter
$1,299/ quarter, from
- 32 CPU cores
- 384GB memory
- 1TB storage
GPU options
- RTX 5070 12GB
For test environments, proving out pipelines, and mid-size GPU jobs.
Ask about this planGPU Advanced
$2,799/ quarter, from
- 40 CPU cores
- 512GB memory
- 1TB storage
GPU options
- RTX 5080 16GB
- RTX 4080S 32GB
For continuous training, inference, and data processing.
Ask about this planGPU Pro
$5,238/ quarter, from
- 60 CPU cores
- 768GB memory
- 1TB storage
GPU options
- RTX 5090 32GB
- RTX 4090 48GB
For larger models, heavier jobs, and multiple users.
Ask about this planGPU models, VRAM, and available configurations are confirmed during consultation.
Cost comparison
Pay-as-you-go fits short tests. When a job runs for months, a fixed quarterly bill is far easier to budget.
Buy a GPU server
Purchase and maintain the whole machine
- Upfront cost
- Buy GPUs and a full server
- Cost structure
- One large purchase
- Environment setup
- Install and maintain it yourself
- Hardware maintenance
- On you
Public cloud on-demand
For ad-hoc tests and short jobs
- Upfront cost
- None
- Cost structure
- Adds up by the hour, bills float with market rates
- Environment setup
- Usually self-serve
- Hardware maintenance
- On the cloud vendor
Quarterly GPU rental
For continuous training and long-running projects
- Upfront cost
- None
- Cost structure
- Fixed quarterly bill, easy to budget
- Environment setup
- We help configure
- Hardware maintenance
- On us
Public cloud on-demand · vast.ai RTX 5090
Billed hourly- Market rate around $0.47 / hour (Sept 2026, fluctuates)
- Running 24/7 for a year ≈ $4,100
- Bills grow with runtime and spot prices — popular cards take hunting, environments are on you
Quarterly GPU rental · this page
Fixed quarterly bill- GPU Starter from $1,299 / quarter — about $5,196 a year
- Term and price locked up front — a fixed bill you can budget around
- Environment setup included, engineers on call
On-demand pricing estimated from the low end of vast.ai market rates as of Sept 2026 — actual prices float. Rental pricing depends on the GPU model and configuration confirmed before provisioning.
Use cases
Training & fine-tuning
Run deep learning training, parameter fine-tuning, and experiment comparisons.
LLM inference
Model testing, batch inference, and internal app validation.
Medical imaging
GPU-accelerated image preprocessing, segmentation, and detection.
Scientific computing
Run research software and pipelines that support GPU acceleration.
Which way to go
Most long-running GPU work starts with a quarterly rental; once scale and timeline are fully fixed, buying a device outright is the better deal.
Recommended
Quarterly cloud GPU rental
Training and inference that run for months fit quarterly rental best: a fixed bill, a preconfigured environment, and engineers on call.
View rental plansUpgrade path once usage stabilizes
On-premise GPU device
When workloads and team size are fully stable, buying a dedicated device outright stops the recurring rent.
Explore local devicesTell us about your workloads
Send us your model, data size, and timeline. We’ll recommend a configuration based on VRAM, memory, and budget.
No need to know server specs up front
GPU model and environment confirmed before launch
FAQ
Not sure which GPU to pick?
Send us your model, framework, dataset size, and expected runtime — we’ll recommend based on VRAM and budget.
When is the environment confirmed?
Before launch. Send us your model, framework, and dependencies — we configure and verify the environment before delivery.
Which compute environments are supported?
CUDA, Python, Conda, PyTorch, TensorFlow, and Jupyter can be configured per job; versions are confirmed before provisioning.
Does it work for multiple users?
Yes — for one person or a team, we configure shared or isolated environments based on headcount and workloads.