Make room for the model
Memory, context length, and throughput determine whether an inference workload fits the system you plan to run.
AI INFERENCE
Put AI into production on dedicated GPU capacity.
Run inference on dedicated NVIDIA GPU systems configured around model memory, network, and capacity needs. Pair compute with a clear reservation plan as workloads move into production.
THE INFERENCE CHALLENGE
Production demands more than a GPU.
A model in production depends on the fit between memory, data movement, and capacity. Planning those pieces together gives teams a clearer path from deployment to growth.
Keep every request moving
Serving depends on a dependable path between model data, compute, and the storage your application needs.
Prepare for changing demand
Reservation terms, available capacity, and a clear expansion plan help teams make informed production decisions.
HOW IT HELPS
Give production workloads a foundation they can grow on.
MODEL MEMORY01
Match GPU memory to the model
Compare memory per GPU and node configurations against model size, context length, and serving requirements.
Explore GPU systemsDATA PATH02
Keep the serving stack connected
Plan compute, network, and storage together for the way requests and model data move through your service.
Explore network and storageCAPACITY PLAN03
Make room for production growth
Review published reservation rates and dedicated capacity options with a clear view of your project timeline.
View pricing and reservations
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
NEVER BUILD ALONE
People who know the stack, within reach.
Bring your model, capacity goals, and timeline. TensorBay’s team can help shape the configuration and next steps.
Talk to sales
- 01A configuration built around your project
- 02A proposal with a delivery timeline
- 03Acceptance testing before you start
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.
| GPU and memory | Monthly termCapacity reserved on a monthly term | Yearly termLower hourly rate |
|---|---|---|
NVIDIAHGX B300288 GB HBM3e per GPU | Monthly from Quote monthlyUS$ 83.20/h | Yearly from Quote yearlyUS$ 58.50/h |
NVIDIAHGX B200180 GB HBM3e per GPU | Monthly from Quote monthlyUS$ 72.80/h | Yearly from Quote yearlyUS$ 50.70/h |
NVIDIAHGX H200141 GB HBM3e per GPU | Monthly from Quote monthlyUS$ 44.20/h | Yearly from Quote yearlyUS$ 31.20/h |
NVIDIAHGX H10080 GB HBM3 per GPU | Monthly from Quote monthlyUS$ 28.60/h | Yearly from Quote yearlyUS$ 19.50/h |
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 specialistStorage
- 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.
Get my proposalIs 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.
EARLY RESERVATIONS
NVIDIA VR200 NVL72
Review the Vera Rubin rack-scale configuration and its early reservation timeline.
NVIDIA VR200 NVL72
GPU COMPUTE
Find the right NVIDIA GPU
Compare TensorBay GPU configurations from the newest generation to the previous ones.
Find the right NVIDIA GPU
PRICING
Plan with published rates
Compare monthly and yearly reservations for GPU nodes and rack-scale systems.
Plan with published rates08Give 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