Next generationVera Rubin VR200 · Early reservations for March 2027
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AI MODEL TRAINING

Train ambitious models at scale.

Build training capacity around your model, data path, and timeline. Dedicated NVIDIA GPU systems, high-speed networking, and clear reservation pricing give your team a foundation from the first run to production.

Plan a training clusterCompare GPU systems
Open GPU server racks in a data center aisle
AI MODEL TRAINING · GPU CLUSTER

THE TRAINING BOTTLENECKS

When training slows, progress stalls.

Long runs depend on more than GPU count. Data movement, system fit, and recovery planning all shape how quickly a team can test an idea and learn from it.

  1. GPUs need a steady data path

    Datasets and checkpoints can leave valuable GPU capacity waiting when storage and networking are planned separately.

  2. One model can outgrow a generic cluster

    Memory, interconnect, and node count need to work together for the model and the way your team trains it.

  3. Recovery is part of the training plan

    An interruption can affect a long experiment. Acceptance checks and a clear recovery path help teams plan for continuity.

HOW IT HELPS

Keep every part of the training run connected.

  • DATA FLOW01

    Keep data close to compute

    Plan the network and storage path with your GPU capacity so datasets and checkpoints fit the way your team works.

    Explore network and storage

  • GPU CAPACITY02

    Choose a system for your model

    Compare dedicated 8-GPU nodes and rack-scale configurations by memory, interconnect, and workload needs.

    Explore GPU systems

  • ACCEPTANCE03

    Start with evidence

    Review load, memory, network, and thermal test results before your cluster is handed over.

    Review acceptance testing

TEAMS USING TENSORBAY

Behind the teams turning AI into business impact.

  • Bank of America
  • Halliburton
  • BRF
  • PicPay
  • BMG Bank
  • City of Rio de Janeiro
  • CNSesi
  • AICUBE
  • AirCloud

TESTED BEFORE HANDOVER

Start with a cluster you can trust.

Every cluster goes through 72 hours of load, memory, network, and thermal tests. Your team reviews the results before approving delivery.

72h
ACCEPTANCE TESTING

Load · memory · network · thermal checks

Talk with our team

AI WORKLOADS

  • TENSORBAY AIFACTORY
    Model trainingDedicated GPUs and high-speed networking for long training runs.
  • TENSORBAY AIFACTORY
    Fine-tuning & research8-GPU nodes for adapting models and iterating on experiments.
  • TENSORBAY AIFACTORY
    Production inferenceMemory-rich systems for serving models on dedicated infrastructure.

05Invest in your next move

Compute to grow. Clarity to invest.

Choose your reservation term, compare capacity, and plan your investment. Here, the conversation starts with open pricing.

USD per hour, complete 8-GPU node

GPU nodes, USD per hour, complete 8-GPU node
GPU and memoryMonthly termCapacity reserved on a monthly termYearly termLower hourly rate
NVIDIAHGX B300288 GB HBM3e per GPU
Monthly
from
US$ 83.20/h
Quote monthly		Yearly
from
US$ 58.50/h
Quote yearly
NVIDIAHGX B200180 GB HBM3e per GPU
Monthly
from
US$ 72.80/h
Quote monthly		Yearly
from
US$ 50.70/h
Quote yearly
NVIDIAHGX H200141 GB HBM3e per GPU
Monthly
from
US$ 44.20/h
Quote monthly		Yearly
from
US$ 31.20/h
Quote yearly
NVIDIAHGX H10080 GB HBM3 per GPU
Monthly
from
US$ 28.60/h
Quote monthly		Yearly
from
US$ 19.50/h
Quote yearly

The hourly rate covers the complete 8-GPU node. Choose a term to request your quote.

SLA
99.9% monthly + 15-min node replacement + goodput credits
Scale
128–10,000+ GPUs · dedicated fabric per tenant
Billing
Per-minute metering inside the reservation · $0 ingress

Every detail accounted for

Your infrastructure. No blind spots.

See what is included and explore rates for storage, data transfer, and networking.

Talk to a specialist
Storage
  • Local NVMe scratch — 30 TB per nodeIncluded
  • Parallel filesystem — managed WEKAdedicated per cluster$0.09/GB-mo
  • Object storage — hot, S3-compatibleNVMe-cached, co-located$0.055/GB-mo
  • Object storage — archive$0.015/GB-mo
  • Snapshots$0.021/GB-mo
Data transfer
  • Ingressalways$0
  • Egress included20 TB/node-mo
  • Egress overageall sites$1.25/TB
  • Inter-node / east-westInfiniBand fabric$0
Networking
  • East-west InfiniBand fabricdedicated per clusterIncluded
  • Private VLAN / VPCIncluded
  • Additional public IPv4IPv6 free$4/IP-mo
  • BYO-IP — /24 v4 or /48 v6$200 setupFree
  • Managed firewall$4/node-mo
  • DDoS protectionIncluded
  • Direct Connect 10G / 100G100G free on yearly terms$450 / $1,800-mo

USD prices per 8-GPU HGX node-hour; rack-scale systems are quoted per rack. Taxes excluded. Managed Slurm and Kubernetes control planes are included. Storage and Direct Connect follow the rates above. Ask about long-term contract options.

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Frequently asked questions

Your next cluster starts with clarity.

Talk to a specialist
Is GPU pricing per GPU or per node?

Published HGX prices are in USD per hour for a complete node with 8 GPUs, within a monthly or yearly reservation. NVL72 systems are quoted per rack. Taxes are not included.

Which NVIDIA GPU should I choose for my AI project?

Compare GPU memory, network requirements and your training or inference workload. The hardware section lists H100, H200, B200, B300 and full rack systems. Our team can help size a configuration based on your model, dataset and capacity requirements.

Can I use a dedicated cluster for training, fine-tuning and inference?

Yes. TensorBay offers dedicated NVIDIA GPU infrastructure for model training, fine-tuning and inference. You can work with Slurm, Kubernetes or SSH and configure the software environment for your workload.

What happens after I request a quote?

Share the GPU, capacity and reservation term you need, or describe your project. Our team reviews the requirements and prepares a proposal within 48 hours, including configuration and delivery timing. Capacity and final terms are confirmed in the proposal.

EXPLORE TENSORBAY

Compute for what comes next.

Explore the GPU systems, infrastructure, and pricing that can move your AI project forward.

GPU server racks extending down a data-center aisle, with overhead cable trays

EARLY RESERVATIONS

NVIDIA VR200 NVL72

Review the Vera Rubin rack-scale configuration and its early reservation timeline.

NVIDIA VR200 NVL72
Close view of a GPU server rack with cooling and data cables inside a data center

GPU COMPUTE

Find the right NVIDIA GPU

Compare TensorBay GPU configurations from the newest generation to the previous ones.

Find the right NVIDIA GPU
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PRICING

Plan with published rates

Compare monthly and yearly reservations for GPU nodes and rack-scale systems.

Plan with published rates

08Give your idea room to grow

Your next AI breakthrough starts with a conversation.

Bring us your challenge. Our team will help define your GPUs, capacity, and timeline. Get a proposal within 48 hours to launch your project or take it further.

  • A configuration built around your project
  • A proposal with a delivery timeline
  • Acceptance testing before you start
tensorbay@tensorbay.io