High-density GPU servers in a modern data center

Dedicated GPU rental for AI teams

Dedicated 8x RTX 4090 Servers

Provisioned for AI training, LLM fine-tuning, image/video generation and batch inference with SSH, Docker, CUDA, PyTorch and per-GPU container delivery.

Launch price from
$2.99/h
Standard 8x4090
$3.49/h
Monthly from
$2,499

Current offer

8x RTX 4090, sold as a usable machine

Launch pricing is designed for the first overseas customers who need a real 8x4090 machine now. Standard and managed tiers keep room for setup, monitoring and support.

Manual availability check

Dedicated 8x RTX 4090

One physical server reserved for your workload. Suitable when the team needs all GPUs on one host and predictable access during a rental window.

  • GPU8x RTX 4090
  • VRAM192GB total
  • AccessSSH / Docker / Jupyter
  • RuntimeCUDA + PyTorch
Plan GPU Price Best for
Launch Test 8x 4090 from $2.99/h first customers
Managed Setup 8x 4090 from $4.49/h Docker / Jupyter
Monthly 8x 4090 from $2,499/mo steady workloads

Market reality

Public marketplaces show the gap: price is one part, inventory is the harder part.

Provider 8x4090 price Status observed Note
NorthGPU launch offer $2.99/h Manual quote Intro price for first customers
NorthGPU standard $3.49/h Manual quote Dedicated host with optional setup support
SynpixCloud $3.39/h No stock in observed screenshot 8x4090 listed, availability limited
Vast.ai sample $5.338/h + bandwidth 4090 search showed no rentable instances Marketplace pricing fluctuates

Customer fit

One GPU, one container

For teams that need separate environments per GPU, we can bind each container to one physical RTX 4090 and expose independent ports for SSH, Jupyter or APIs.

GPU 0 container-01
GPU 1 container-02
GPU 2 container-03
GPU 3 container-04
GPU 4 container-05
GPU 5 container-06
GPU 6 container-07
GPU 7 container-08

Container-level isolation

RTX 4090 does not support NVIDIA MIG. We provide Docker-level GPU binding, isolated runtimes and separate access endpoints. This is suitable for independent jobs, team testing and service instances.

$ docker run --gpus '"device=0"' pytorch/pytorch:cuda

Trust package

What we show before a paid test

01

Hardware proof

nvidia-smi for all 8 GPUs, CPU, RAM, disk and driver version screenshots.

02

Runtime proof

CUDA, PyTorch and Docker validation with a small benchmark or customer-provided test.

03

Network proof

Bandwidth and latency samples for customer regions, with bandwidth terms quoted separately.

04

Delivery proof

SSH, Jupyter or API endpoint handoff with a paid two-hour test before longer rental.

Process

From inquiry to running workload

  1. 01Send GPU count, rental length, workload type and region sensitivity.
  2. 02We confirm available machine, quote and bandwidth terms.
  3. 03You book a paid two-hour test or deposit for longer rental.
  4. 04We deliver SSH, Docker containers, ports and runtime environment.
  5. 05Your team validates and extends to daily, weekly or monthly rental.

FAQ

Questions overseas teams ask first

Is the machine physically located in Europe?

Some workloads require EU residency or low latency. If EU location is mandatory, we state that upfront. If remote training is acceptable, the machine can still be useful for European customers.

Can each GPU run as a separate instance?

Yes, through Docker-level GPU binding and separate ports. RTX 4090 does not support MIG hardware partitioning, so we do not describe it as MIG isolation.

Do customers get root access?

We can provide root or managed access depending on risk profile, rental duration and accepted usage.

How is bandwidth charged?

Bandwidth is confirmed during quote. Public marketplaces often list GPU price plus bandwidth, so the final comparison should include traffic needs.

Sales inquiry

Check 8x4090 availability

Tell us your GPU count, target rental length, software stack and whether the workload requires a specific region. We will reply with available inventory, launch pricing and a paid two-hour test option.

Form opens your email client with a prefilled availability request.