Your AI model is ready. Is your infrastructure?

Move your AI workloads from POC to production with the right compute, architecture and capacity.

  • Training
  • Fine-tuning
  • Inference
  • GenAI
  • AI Agents
  • Computer Vision
  • AI SaaS

Have an AI infrastructure requirement?

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Or share your requirement in a short form
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Front view of two GPU server racks coming online

Before you choose a GPU, understand the workload

AI infrastructure is not just about selecting a GPU. The right architecture depends on what you are building, when you want to go live, where the workload needs to run, how much scale you need, and the performance and economics you expect.

Watch this short video to understand how to approach AI infrastructure before moving from POC to production.

  1. WhatWhat AI workload are you running?
  2. WhenWhen does it need to move into production?
  3. WhereWhere does the workload need to run?
  4. ScaleHow much compute and capacity will be required?
  5. WhyWhat business outcome are you trying to achieve?

The right GPU is the outcome of the conversation, not the starting point.

Planning an AI workload? If your AI project is moving from POC to production, our team can help you evaluate the right compute, architecture, capacity, location, performance, and cost.

Share Your Requirement

Planning production AI in the next 30–90 days? Start the infrastructure conversation early.

GPU type, capacity, architecture, location, storage, networking and deployment timelines can materially affect performance, availability and economics. The earlier these are settled, the more options you have.

Share your timeline with us

Don’t start with the GPU.
Start with the workload.

AI infrastructure decisions should start with the workload, not a GPU model number. We help organizations evaluate the architecture, compute capacity, deployment location and economics required to move AI from experimentation to production.

  1. 1WorkloadWhat you are running, and at what scale
  2. 2ModelSize, architecture and precision
  3. 3GPUClass and generation that fit the model
  4. 4MemoryGPU memory per card and host RAM
  5. 5ClusterNode count and interconnect
  6. 6StorageThroughput for datasets and checkpoints
  7. 7NetworkFabric, bandwidth and egress
  8. 8LocationLatency, residency and compliance
  9. 9CostCommercial model and total cost

Start at step one. Tell us what you are running and we will work through the rest with you.

Share Your Requirement

What are you building?

Each stage of the model lifecycle puts different demands on compute, memory and storage. Pick the one closest to your plan.

AI Applications

Infrastructure for GenAI, AI agents, vision, speech, LLM applications and AI-native SaaS.

Discuss an AI application

Production AI is an infrastructure problem, not just a GPU problem.

A model that runs well in a notebook still needs storage that keeps up, a network that doesn’t bottleneck, a place to run that meets your compliance needs, and a cost profile that holds at scale.

The GPU is one layer of the stack. We plan all of them together.

Plan the Full Stack With Us
Economics
Cost optimization
Operations
ScalingMonitoring
Orchestration
Kubernetes
Placement
Deployment locationData sovereignty
Network
Networking
Data
High-performance storage
Compute
GPU computeGPU clustersCPU and memory

Why ZNet

An infrastructure partner that starts with your workload and stays with you through deployment.

Talk to Your Regional Team

  • Access to multiple infrastructure options

    Recommendations follow your workload, not a single platform we need to fill.

  • Workload-led architecture consultation

    Compute, memory, storage and network sized around what you are actually running.

  • Training, fine-tuning and inference

    Infrastructure for every stage, from first training run to production traffic.

  • India and global deployment

    In-country options for data residency, or regions closer to your users worldwide.

  • Technical and commercial consultation

    Architecture and pricing discussed together, so the design fits the budget.

  • From architecture to deployment

    Backed by ZNet’s cloud and infrastructure team, with one team from the first conversation through go-live.

Talk to your regional team

Prefer a named contact? Reach the AI infrastructure specialist for your region directly.

Outside India, or not sure who to contact?
Share your requirement General ZNet contact

Tell us about your AI workload

A few details help us bring the right specialist to the first conversation.

  1. 1
    We review your requirement

    Workload, timeline and any GPU or location preferences.

  2. 2
    An infrastructure specialist contacts you

    By email or phone, whichever you prefer.

  3. 3
    We map suitable options

    Architecture, capacity, location and commercials.

We’ll connect you with the specialist for your region.

Deployment timeline

GPU requirement

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