RTX 3090 Dense Pods
Multi-GPU towers with four RTX 3090 cards linked via NVLink bridges, 256 GB RAM, and dual NVMe scratch arrays. Perfect for diffusion, fine-tuned LLMs, and computer vision batches that thrive on massive CUDA throughput.
Scale model training with sovereign GPU clouds engineered for throughput.
BHK AI GPU delivers managed NVIDIA RTX 3090 clusters backed by AMD Ryzen Threadripper 3970X compute. Enjoy ready-to-train images, low-latency networking, and direct access to BHK S3 datasets for notebooks, training jobs, and production inference without babysitting hardware.
Compute Fabric Tailored for AI
Our GPU pods center on NVIDIA RTX 3090 GPUs paired with AMD Ryzen Threadripper 3970X hosts. Airflow-tuned chassis, ECC memory, and NVMe scratch volumes keep experiments stable while 64 CPU threads per node chew through data prep and orchestration tasks.
- Dedicated RTX 3090 GPUs delivered via PCIe passthrough for full access to CUDA cores and 24 GB GDDR6X.
- Threadripper 3970X systems with 256 GB RAM to stage datasets, run loaders, and coordinate distributed training.
- Dual 10/25 GbE networking between nodes plus NVMe RAID scratch disks for fast checkpointing.
- Optional NVLink bridges for multi-GPU models and GPU partition isolation for secure multi-tenant usage.
24 GB
GDDR6X VRAM per RTX 3090 for extended sequence and diffusion workloads.
32 Cores
Threadripper 3970X compute per node to accelerate dataloading and orchestration.
25 GbE
Dedicated east-west throughput between nodes for rapid checkpoint syncs.
Cluster Profiles
Hybrid Prep + Training
A single RTX 3090 paired with 32-core Threadripper 3970X CPUs dedicated to data munging, feature generation, and gradient steps in the same box—ideal for teams juggling ETL and model updates without queueing.
Threadripper Build Nodes
CPU-heavy nodes running Threadripper 3970X, 256 GB RAM, and workstation GPUs for compilation, simulation, and multi-container CI that feeds downstream training clusters with reproducible artifacts.
RTX 3090 Serving
Single-GPU inference nodes optimized for TensorRT and ONNX Runtime with autoscale groups, cold-start images, and rolling updates via the BHK Control Plane for micro-batch or streaming responses.
Scheduler & Developer Experience
Jobs run on BHK Managed Kubernetes with GPU-focused enhancements. Our scheduler understands topology, job priority, and cost envelopes, allowing teams to reserve capacity or burst on demand with predictable spend.
- Submit jobs via CLI, REST, or GitOps pipelines with YAML-based job manifests.
- Real-time telemetry with token-level tracing, gradient health, and auto alerting.
- Integrated experiment tracking, model registry, and weight versioning.
- Built-in support for distributed checkpointing and automatic restart workflows.
“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 compared to hyperscale alternatives.”
Lifecycle Timeline
Discovery & Sizing
Collaborate with solution architects to profile workloads, identify bottlenecks, and right-size clusters. We benchmark representative training runs to calibrate throughput expectations.
Provision & Integrate
Launch dedicated clusters with secure network peering to your VPCs. Connect BHK S3 buckets, configure secret stores, and seed base container images tailored to your stack.
Optimize & Scale
Continuous performance reviews ensure kernels, communication libraries, and cluster topology stay tuned. Hit auto-scaling thresholds to elastically add capacity with no queue backlogs.
Co-design your next AI training milestone.
Engage our applied AI and platform engineering teams to architect bespoke GPU fleets, optimize pipelines, and streamline deployment. We specialize in aligning performance with compliance and budget expectations.