Neysa Velocis GPU Cloud Platform

GPU Cloud for Training, Inference, and Everything Between

Run your AI workloads on production-grade GPU infrastructure. On demand or reserved, bare metal or virtualised, from $1.17/GPU hour, with MLOps engineers to support you.

  • 100+ AI teams already building here
  • Proactive support engineers on every account
  • SOC 2, ISO 27001, IRDAI-empanelled
Talk to a GPU cloud expert See pricing
SOC 2 Type II
ISO 27001
IRDAI-empanelled

Trusted by teams building production AI

HDFC Bank
Justdial
BharatGen
CaratLane
ITQ × Travelport
FSS
Juspay
Indian Institute of Science
Jocata

Why Neysa

Neysa GPU Platform is built for the way AI teams actually work

Production-Grade Silicon

NVIDIA B300, RTX Pro 6000, H200, H100, L40S, and AMD MI300X. You always have access to the compute you need for your AI.

Open-Source First

Built on open-source tooling end to end. PyTorch, HuggingFace, vLLM, Kubernetes, Slurm, MLflow. No proprietary stack, no control plane tax.

Transparent Costs

Usage-based billing per GPU hour, a 1 Gbps uplink included, no software layer markup and no surprise invoices. Teams run at 40 to 60% better unit economics than on a general-purpose cloud.

Flexible Deployment

VM, Kubernetes cluster, or bare metal. Your choice. Switch formats as your workload evolves without changing your code or rebuilding your stack.

Security and Compliance Built In

SOC 2 Type II, ISO 27001:2022, ISO 27017, ISO 27018, and IRDAI certified. Zero-trust access, RBAC, and full encryption at rest and in transit.

Engineering Support That Knows AI

Dedicated MLOps engineers help you size clusters, plan capacity and resolve infrastructure issues before they become outages.

Setup

Flexible GPU options, ready to deploy

Build the right setup for your workload. Choose from NVIDIA and AMD silicon, configure storage and networking, and deploy in the format that fits your team.

B300

Blackwell-generation compute, available now.

  • 288 GB HBM3e memory per GPU, 2,304 GB per node
  • Blackwell Ultra architecture for frontier-scale training and inference
  • Built for the largest models and highest-throughput serving
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RTX Pro 6000

Blackwell-generation compute for inference and visual workloads.

  • 96 GB GDDR7 memory
  • Blackwell architecture with fifth-generation tensor cores
  • Strong price-performance for inference, fine-tuning and rendering
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H200

More memory for models that need room to grow

  • 141 GB HBM3e memory
  • Higher memory bandwidth than H100
  • Energy-efficient at production scale
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H100

Built for enterprise training and large-scale inference

  • 80 GB HBM3 memory
  • PCIe and SXM versions available
  • Multi-instance GPU partitioning
  • NVLink high-bandwidth interconnect
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L40S

Cost-efficient compute for startups and research teams

  • Enhanced tensor core performance
  • Native CUDA, PyTorch, and TensorFlow integration
  • Cloud-ready and scalable in clusters
  • Cost-efficient for iterative experimentation
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AMD MI300X

High memory density for demanding AI workloads

  • 192 GB HBM3 unified memory
  • High aggregate memory bandwidth
  • ROCm support with PyTorch and JAX
  • Optimized for large model inference
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AI-ready

Everything you need from a GPU provider , in one place

Widest GPU lineup

NVIDIA B300, RTX Pro 6000, H200, H100, L40S and L4, plus AMD MI300X GPUs available to deploy.

We love open source

Open-source stack from infrastructure to orchestration. PyTorch, vLLM, Kubernetes, Slurm, MLflow. Your models, configs and data are portable.

Engineering Support That Knows AI

Dedicated MLOps engineers help you size clusters, plan capacity and resolve infrastructure issues before they become outages.

Data Sovereignty

India deployments for teams with data residency requirements. Your model IP and training data stay resident in India, in a single-tenant environment dedicated to your workload.

Pricing

What a cloud GPU costs on Neysa

GPU Memory Best for From
NVIDIA B300 288 GB HBM3e Frontier-scale training and highest-throughput serving On request
NVIDIA RTX PRO 6000 96 GB GDDR7 Inference, fine-tuning and visual workloads On request
NVIDIA H200 141 GB HBM3e Large-model training and high-throughput inference $2,111 · ₹1,89,974 /mo · 1-GPU VM
NVIDIA H100 80 GB HBM3 Enterprise training and large-scale inference $2,013 · ₹1,81,138 /mo · 1-GPU VM
AMD MI300X 192 GB HBM3 Large-model inference $14,692 · ₹13,22,288 /mo · 8-GPU node
NVIDIA L40S 48 GB Fine-tuning and cost-sensitive inference $816 · ₹73,435 /mo · 1-GPU VM
NVIDIA L4 24 GB Light inference, dev and test $490 · ₹44,061 /mo · 1-GPU VM

H100 and H200 also come as 8-GPU bare-metal nodes, from $14,059 (₹12,65,315) and $15,630 (₹14,06,691) per month.

Included in the GPU services

The GPU, local NVMe on bare-metal nodes, and the interconnect.

Billed separately

Storage, public IPs, bandwidth commitment.

Start Building on Neysa GPU Cloud Platform Today

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FAQs

Your Questions Are Answered Here

Neysa is an India-based GPU cloud provider. We build and run the Neysa Velocis cloud platform ourselves, so the cloud GPU services you buy, from compute and orchestration to observability and governance, work as one system.

We start with a short call to understand your workload, team setup and requirements. From there we put together a configuration and a written rate, usually the same week. Then we stand up the cluster and the fabric and hand over access, and you run your jobs on it.

Usage-based billing per GPU hour, with no hidden egress fees and no software layer markup. On-demand rates start at $1.17 per GPU hour. Reserved pricing is available for predictable workloads on terms from one month to sixty, and the rate drops as the term lengthens. Starting rates are published above and we will walk through your exact configuration on the first call.

Both. On-demand hourly for bursts, evaluation and inference that comes and goes. Reserved monthly commitments for steady training and production inference, which is where the economics land for most teams running continuously.

A GPU VM gives you one, two or four GPUs on a virtualised host, which is the fastest way to get a single box running. A bare-metal node gives you all eight GPUs with no virtualisation layer and local NVMe included, which is what long training runs and latency-sensitive inference want. You can move between them without changing your code.

Yes. You can deploy any Hugging Face model, bring your own Docker images, and connect your existing CI/CD pipelines. We do not require you to use proprietary tooling.

All plans include access to our engineering support team. Active customers with production workloads get prioritised response times, and MLOps guidance is available on request. Engineers help with cluster sizing, capacity planning and infrastructure issues. Work inside your own training code, model architecture or application layer stays with your team.

In Indian data centres, in a single-tenant environment dedicated to your workload. Your model IP and training data stay resident in India.

1 Gbps uplink included, no charge for egress within it.