GPU Compute Built
for Serious AI.
NVIDIA RTX 3090 · 24 GB GDDR6X · 10,496 CUDA cores, provisioned via clean API in under 60 seconds, billed by the hour, zero enterprise lock-in.
- 24 GBGDDR6X VRAM
- $0.06per GPU hour
- < 60sprovisioning time
- 10496CUDA cores
Built for teams training, serving, and shipping AI at scale
Compute Fabric
RTX 3090 compute, ready in under a minute.
24 GB GDDR6X memory, 10,496 CUDA cores, and tensor performance tuned for modern AI, backed by AMD Threadripper 3970X hosts with 256 GB RAM and 25 GbE east-west networking.
- Dedicated PCIe passthrough for full CUDA access, no sharing
- NVLink bridges available for multi-GPU workloads
- Dual NVMe RAID scratch volumes for fast checkpoints
- Pre-baked images: PyTorch 2.3, TensorFlow 2.16, CUDA 12.4
- 64 host threads for data loading and distributed coordination
BHK_API_KEY before calling
# export BHK_API_KEY=bhk_sk_live_4a2e8b1c9d7f3a5b
$ bhk gpu launch --type rtx3090 --image pytorch-2.3
→ instance gpu-node-07 ready in 38sHow It Works
From zero to training in four steps.
Provision, submit, monitor, and scale, all through the API.
Provision a GPU Node
One API call or CLI command. Choose instance type, image, and region. The node is live in under 60 seconds.
Submit Your Job
Push a training script or inference container via CLI, REST, or GitOps manifest. Jobs start immediately on your provisioned node.
Monitor in Real Time
Stream GPU utilization, VRAM usage, loss curves, and job logs from the dashboard or via the metrics API endpoint.
Scale or Terminate
Add nodes for distributed runs or terminate the instant your job is done. Billing stops to the second with no idle charges.
Cluster Profiles
Match the shape of your workload.
Six purpose-built profiles. Pick one or compose your own with the API.
RTX 3090 Dense Pods
4× RTX 3090 linked via NVLink, 256 GB RAM, dual NVMe scratch arrays. Built for diffusion, fine-tuned LLMs, and computer-vision batches.
Hybrid Prep + Training
Single RTX 3090 with a 32-core Threadripper for ETL, feature generation, and gradient steps in one box, ideal for solo ML engineers.
Threadripper Build Nodes
CPU-heavy nodes for compilation, simulation, and CI pipelines that feed downstream training clusters with reproducible artifacts.
RTX 3090 Inference Serving
Single-GPU nodes optimized for TensorRT and ONNX Runtime. Autoscale groups, cold-start images, rolling updates via the BHK Control Plane.
Managed Kubernetes Scheduler
GPU-aware topology scheduling with priority queues, cost envelopes, and burst-on-demand. Submit via CLI, REST, or GitOps YAML manifests.
Direct-from-S3 Dataset Streaming
Mount BHK S3 buckets directly to GPU nodes. Stream training datasets, write checkpoints, and retrieve model weights without leaving the cluster network.
Scheduler & DX
Schedule, observe, and ship faster.
Jobs run on BHK Managed Kubernetes with GPU-focused enhancements. The scheduler understands topology, priority, and cost envelopes so you can reserve capacity or burst on demand with predictable spend.
- Submit via CLI, REST, or GitOps with YAML manifests
- Real-time telemetry: token-level tracing, gradient health, auto alerting
- Built-in experiment tracking, model registry, and weight versioning
- Distributed checkpointing and automatic restart on node failure
- Cost envelopes with per-job spend caps and utilization alerts
"BHK Cloud helped us take a 30B-parameter model from prototype to production in six weeks, with deterministic run times and a 40% cost reduction versus hyperscale alternatives." Head of ML, undisclosed customer
Why BHK Cloud
Transparent pricing. No surprises.
We stripped the enterprise overhead so your GPU spend goes to compute, not cloud markups.
| Feature | BHK Cloud | AWS p3.2xlarge | Lambda Labs | RunPod |
|---|---|---|---|---|
| GPU / hour (on-demand) | $0.06 – $0.10 | $3.06 | $0.50 | $0.34 – $0.44 |
| VRAM | 24 GB GDDR6X | 16 GB HBM2 (V100) | 24 GB (3090) | 24 GB (3090) |
| Provisioning time | < 60 seconds | 3 – 8 minutes | 1 – 3 minutes | 30 – 90 seconds |
| Minimum commitment | None | Often reserved | None | None |
| Pre-baked ML images | PyTorch, TF, CUDA | Via AMI marketplace | PyTorch, JAX, TF | PyTorch, TF, CUDA |
| API complexity | Single clean REST API | Dozens of services | Simple REST | GraphQL + REST |
| Integrated object storage | BHK S3 · $0.99/TB | S3 billed separately · $23/TB | No native storage | Network volumes only |
Pricing sourced from public on-demand rates as of May 2026. AWS p3.2xlarge uses V100 16 GB. Actual costs vary by region and usage pattern.
FAQ
GPU hosting, answered.
Everything you need to know before running your first workload.
What workloads run well on the RTX 3090?
The RTX 3090's 24 GB VRAM makes it excellent for LLM inference (models up to ~30B parameters in 8-bit), image generation (Stable Diffusion XL, ComfyUI), model fine-tuning with LoRA/QLoRA, batch rendering, and video encoding. It handles modern diffusion models that require 18–22 GB of VRAM comfortably and outperforms older V100 nodes on FP32 throughput.
How does hourly GPU billing work?
You are billed for every hour your GPU node is running. There are no minimums. Spin up for a single experiment and terminate when done. Billing stops the moment you call bhk gpu terminate or use the dashboard. Fractional hours are billed pro-rata to the second.
How fast is provisioning?
Typical provisioning time is under 60 seconds from API call to SSH-accessible node. Pre-baked images for PyTorch 2.3, TensorFlow 2.16, and bare CUDA 12.4 are cached on-host, so image pull is near-instant. You can be running a training job within two minutes of your first API call.
Can I run multi-GPU distributed training?
Yes. Our Dense Pod profile supports 4× RTX 3090 linked via NVLink on a single node, which covers most distributed training needs below 96 GB of aggregate VRAM. For larger multi-node runs, contact our engineering team to configure a dedicated cluster with high-bandwidth east-west networking.
How do I load datasets from BHK S3 storage?
GPU nodes are co-located with BHK S3 on the same internal network. Set --endpoint-url https://s3.bhkcloud.com in your AWS CLI or boto3 config and mount the bucket directly. You'll see transfer speeds of 2–4 GB/s on large dataset reads, fast enough to stream most training sets without pre-staging to local NVMe.
Do you offer reserved capacity or enterprise SLAs?
Yes. If you need guaranteed node availability, dedicated hardware, uptime SLAs, or volume-based pricing discounts, talk to our team. We'll put together a capacity plan based on your training schedule and budget envelope, typically scoped within one business day.
Ready to run your first GPU job?
Tell us about your workload and we'll provision the right cluster to get you training within the hour.