High-performance RTX 3090 compute and S3-compatible storage. Transparent hourly pricing, a clean API, zero enterprise overhead.
GPU Hosting
24 GB GDDR6X memory, 10,496 CUDA cores, and tensor performance tuned for modern AI workloads, provisioned through a clean API, priced by the hour.
BHK_API_KEY first
# export BHK_API_KEY=bhk_sk_live_4a2e8b1c9d7f3a5b
$ bhk gpu launch --type rtx3090
→ instance gpu-node-01 ready in 42sAPI Storage
S3-compatible object storage tuned for model assets, application data, and high-throughput pipelines with predictable per-TB pricing.
# Access Key: BHK4A2E8B1C9D7F3A5B6 Secret: bhkSk7x9mK2pL5qR8sV4wN1j
$ aws s3 cp ./model.bin s3://ml-checkpoints/ \
--endpoint-url https://s3.bhkcloud.com
upload: 14.2 GB in 9.1sHow It Works
From first contact to running GPU workloads in under two minutes.
Reach out via our contact form or schedule a demo call. We'll configure the right plan for your workload and provision your account.
Spin up GPU nodes and storage buckets with our clean REST API or CLI. Pre-baked images get you from zero to training in seconds.
Pay only for compute and storage you actually use, billed by the hour. No minimums, no commitments. Scale down anytime.
Pricing
No hidden fees. No annual commits. No enterprise lock-in.
Hourly compute for AI, inference, and rendering.
S3-compatible storage for data, models, and backups.
Dedicated capacity, SLAs, and priority support.
All plans include API access and usage-based billing. Need committed capacity? Talk to us →
Why BHK Cloud
We cut the enterprise overhead so you get better pricing and a faster developer experience.
| Feature | BHK Cloud | Hyperscaler |
|---|---|---|
| RTX 3090 GPU / hour | $0.06 – $0.10 | $0.90+ |
| Object storage / TB | $0.99 – $2.49 | $23+ |
| Minimum commitment | None | Often required |
| GPU deployment time | < 60 seconds | 2 – 10 minutes |
| API complexity | Clean, simple REST | Dozens of services |
| S3 compatibility | ✓ Drop-in compatible | ✓ Native |
Hyperscaler rates based on public on-demand pricing as of 2026. Actual savings vary by usage pattern.
Platform
A focused infrastructure stack without the enterprise bloat.
24 GB VRAM and 10,496 CUDA cores ready for training, inference, and rendering pipelines.
Spin up nodes, attach volumes, and stream data through clean, well-documented endpoints.
Grow from gigabytes to petabytes without re-architecting. Pay only for what you use.
Starting at $0.06/hr, built so experimentation never gets billed like enterprise production.
CLI, REST API, and infra-as-code patterns. No clicking through 40 settings panels.
Encrypted data at rest and in transit, regional durability, and strong consistency guarantees.
FAQ
Everything you need to know before getting started.
You're billed for every hour (or fraction thereof) your GPU node is running. There are no minimum commitments. Spin up for a single experiment and shut down when done. Billing stops the moment you terminate the instance.
Yes. BHK Cloud storage is fully S3-compatible, so you can point your existing AWS CLI, boto3, or any S3 SDK at our endpoint without changing your code. Just set --endpoint-url https://s3.bhkcloud.com.
Typical deployment time is under 60 seconds. Pre-baked images for PyTorch, TensorFlow, and CUDA are ready to go, so you can be running training or inference jobs within two minutes of launch.
Yes. If you need reserved GPU nodes, dedicated hardware, custom SLAs, or volume-based pricing, contact our team. We'll put together a custom proposal based on your workload and timeline.
The RTX 3090's 24 GB VRAM makes it excellent for LLM inference (up to ~30B parameter models), image generation (Stable Diffusion, ComfyUI), model fine-tuning, batch rendering, and video encoding. It's well-suited for anything that fits within 24 GB of VRAM.
Fill in the contact form or schedule a demo call. We'll set up your account, walk you through the API, and have you running within the same business day.
Tell us about your workload and we'll get you running on the right setup in minutes.