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Compute · GPUs

Cloud GPUs for AI, rendering and scientific computing

Accelerate the workloads that punish CPUs — model training, 3D rendering, video encoding and simulation — on GPU instances you rent by the month instead of buying by the rack.

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Launching soonFlexible configurationsReserve capacity early
portal.antyxsoft.io
GPU instance · preview
Accelerator
Current-generation, one per instance
Waitlist
vCPU · RAM · NVMe
Sized around the accelerator, not fixed to it
Configurable
Regions
Boston first, then Athens and Barcelona
Staged
Billing
Monthly, on the same invoice as the rest of your estate
Flat rate

Why teams choose cloud GPUs

Cutting-edge accelerators without the capital expenditure, the procurement lead time or the depreciation.

Accelerated computing

Cut complex computation and data-processing times dramatically, reducing time to insight.

No hardware investment

Avoid the upfront cost of on-premises GPU hardware and pay only for the capacity you use.

High performance

Handle the most demanding computational tasks on current-generation accelerator hardware.

Elastic scalability

Scale GPU resources up for a training run and back down when it finishes.
Architecture

Built for availability, not just peak throughput

The GPU platform is architected for high availability and consistent performance, giving reliable, efficient access to accelerator resources rather than best-effort capacity that disappears when demand spikes.
Reliable, efficient access to GPU resources
Flexible configuration options per workload
Instance resources matched to the accelerator, not fixed to it
GPU capacity · planned
RegionStatus
Boston · us-bos-1
First accelerator pool
Provisioning
Athens · eu-ath-1
First EU region
Planned
Barcelona · eu-bcn-1
Second EU region
Planned
vCPU · RAM · NVMe
Sized around the accelerator, not fixed to it
Configurable
Capacity is reserved per region rather than pooled best-effort — waitlist accounts are allocated first.
Monitoring

Real-time visibility into every GPU you run

Live utilisation and performance metrics in the same control panel as the rest of your infrastructure, so an under-fed GPU or a stalled training run is obvious long before the invoice arrives.
Real-time GPU utilisation and performance metrics
Project-level separation for teams and experiments
Flexible billing — prepaid credit or monthly invoicing
GPU metrics · project ml-research
InstanceGPU util
gpu-train-01
Fine-tune · epoch 12 of 30
97%
gpu-train-02
Dataloader bound · 4 vCPU
41%
gpu-serve-01
Inference · 24 concurrent
68%
gpu-dev-03
Notebook idle for 6h 12m
0%
An under-fed accelerator and a forgotten notebook look the same on an invoice. They do not look the same here.
Launching soon

GPU instances are not generally available yet

Support for GPU instances is being introduced across our regions. Tell us what you plan to run and we will contact you when capacity opens in your region — waitlist accounts are allocated first.

Join the waitlist Talk to sales
Regions
Boston first, then Athens and Barcelona
Billing
Monthly, on the same invoice as the rest of your estate
Pairs with
Block Storage for datasets, Kubernetes for scheduling
Config
vCPU, memory and storage sized around the accelerator

GPU use cases

Anything that parallelises well finishes sooner on a GPU — and finishing sooner is what you are actually buying.

Machine learning

Model training and inference

Train and deploy complex models faster with massively parallel processing, then serve them from the same platform.
Scientific computing

Simulation and analysis

Run compute-bound simulations and large-scale data analysis in a fraction of the CPU-bound wall time.
Creative

3D rendering and visualisation

Accelerate render times for 3D graphics and visual effects so creative iteration is not gated by the farm.
Media

Video encoding and processing

Optimise encoding pipelines for high-quality output with substantially reduced processing time.
Planning a GPU workload? Start with the docs.

Instance creation, storage, networking and quota — the same knowledge base covers everything GPU instances will build on.

Read the docs

Frequently asked questions about Antyxsoft GPUs

Are Antyxsoft GPUs available today?

Not yet. GPU products are not generally available at this time — support for GPU instances is being introduced soon. Join the waitlist and we will contact you as capacity opens in your region.

Which workloads are cloud GPUs suited to?

GPU instances accelerate workloads that rely on parallel processing: machine learning training and inference, scientific computing, 3D rendering and visualisation, and video encoding.

Can I customise a GPU instance?

Yes. GPU instances will offer flexible configuration options so the vCPU, memory and storage around the GPU can be matched to the workload.

How will GPU usage be monitored?

Real-time metrics and performance monitoring are included, so GPU utilisation can be tracked and optimised from the same control panel as the rest of your infrastructure.

All FAQs →

Be first in line for GPU capacity

Tell us what you plan to run and we will reach out the moment your region opens.

Join the waitlist Contact sales