Next-Gen Infrastructure

Unleash Neural Compute.

Provision GPU instances, deploy serverless inference, and manage dynamic volumes through our robust REST API. Experience seamless scaling backed by Edge-native control.

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bolt Instant provisioning
Futuristic GPU hardware infrastructure representing accelerated compute

Compute Ready

H100 Cluster

Latency

0.8ms

Familiar AI stack — your control plane for GPUs and serverless

Logos represent common tooling in the ecosystem, not endorsements.

OpenAI Anthropic Meta HuggingFace Mistral AI

Everything you need to resell GPU capacity

One API surface for pods, serverless inference, storage, and registry auth — proxied from your upstream provider with your markup.

memory

GPU instances on demand

List, create, start, stop, and tear down pods through /instances — mapped cleanly from your provider API.

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Serverless endpoints

Full CRUD over /endpoints with edge-cached reads and automatic invalidation on writes.

savings

Pricing you control

Configurable markup on upstream rates, scraped pricing tables, and a public /pricing feed backed by D1.

security

Auth & audit by default

Argon2 passwords, rotating refresh tokens, JWT access tokens, and per-request audit logs — so you know who called what and when.

Deploy in 3 lines of code

Our flexible multi-tooling approach means you can integrate GPUWorker directly into your Python workflow, trigger it via REST, or deploy custom Docker containers instantly.

  • 1

    Define your environment

    Specify GPUs, memory, and dependencies via SDK, API, or Dockerfile.

  • 2

    Sync local data

    High-speed data volumes mount automatically to your remote containers.

  • 3

    Run at scale

    Distribute training across 100+ nodes instantly.

import gpuworker
from gpuworker import GPU, Volume
# Request H100 infrastructure
@gpuworker.function(gpu=GPU.H100, memory="80GB")
def train_llm(dataset_path):
model = AutoModel.from_pretrained("llama-3-70b")
trainer.fit(model, dataset_path)
# Execute in cloud
if __name__ == "__main__":
gpuworker.run(train_llm, path="/data/s3")
terminal$ gpuworker deploy main.py_
Build complete: 2.1s

Built for modern AI workloads

From fine-tuning foundational models to serving low-latency inference, our infrastructure scales gracefully to meet your exact compute requirements.

model_training

Model Training

Distribute massive training jobs across multi-node H100 clusters with high-bandwidth InfiniBand networking and automatic volume mounting.

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Serverless Inference

Deploy any containerized model as an API endpoint. Auto-scale from zero to thousands of requests per second and pay only for active compute.

labs

Testing & Dev

Spin up interactive PyTorch and Jupyter environments in seconds on fractional GPUs, saving up to 80% on early-stage research costs.

Transparent Comparison

See how representative GPU and serverless rates compare against hyperscalers in real time — then tune the markup for your own marketplace capabilities.

Frequently Asked Questions

Everything you need to know about the platform and billing.

How does serverless billing work? expand_more
You are billed per-second for the active compute time. When your endpoint scales to zero, you pay a significantly reduced idle fee or nothing at all, depending on your configuration.
Can I use my own Docker containers? expand_more
Yes. You can push your custom Docker images to our private registry or provide an auth token to pull directly from your AWS ECR or Docker Hub.
What is the difference between Secure Cloud and Community Cloud? expand_more
Secure Cloud instances are hosted in Tier-3, ISO27001-compliant data centers with strict guarantees. Community instances utilize verified peer-to-peer capacity at massive discounts, ideal for fault-tolerant batch workloads.
How do data volumes work? expand_more
Data volumes are network-attached NVMe storage drives that provide persistent storage across container restarts. You can seamlessly mount them to any active pod.

Ready to break the compute bottleneck?

Spin up in minutes: account, API tokens, and dashboard access. Scale usage with your customers — not with boilerplate.

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