AI / GPU Infrastructure

Managed GPU Infrastructure for AI Teams

Deploy your LLM or training job on H100 and A100 GPUs in India. We handle CUDA, vLLM, and 24/7 ops. You own the model and the results.

Talk to a GPU Engineer See GPU Hardware
L40S to B200-class GPU nodes India datacenter (Mumbai DPDP) trial node before you commit 17 years infra ops since 2009
Running production workloads for
Revolt MotorsPC JewellerRR KabelImpresarioIntentwiseLoomBhimaBGaussMitutoyo
What AI infrastructure means here
GPU hosting
GPU hosting is a service that provisions dedicated NVIDIA GPUs, H100 and A100 in this case, in an India datacenter for AI workloads. ZenoCloud configures CUDA and vLLM on the hardware and runs 24/7 ops, so a team deploys a model or a training job without provisioning drivers or managing the server underneath it.
Managed AI infrastructure
Managed AI infrastructure means the GPU, the CUDA stack, and the serving or training software are operated by the hosting provider rather than the team running the model. ZenoCloud's version runs on managed GPU infrastructure in Mumbai — L40S to B200-class nodes, provisioned and operated by our engineers — with a trial node available before teams commit to a month.
L40S–B200
GPU Classes Managed in India
2009
Running Infra Since
2–7 days
Provisioning Lead Time
24/7
Managed Ops & Monitoring
0
Per-Token Charges

What ZenoCloud Manages

Give us your model. We handle everything else — from bare metal to the API endpoint.

GPU Provisioning

Hardware racked, tested, and benchmarked. NVIDIA-SMI health check and memory bandwidth validation before handoff. CUDA 12.4 + cuDNN 9.0 stack.

Runtime Installation

vLLM, Ollama, TGI, or TorchServe installed and configured for your model family. CUDA, driver compatibility, and NCCL all handled.

OpenAI-Compatible API

You get an HTTPS endpoint at your subdomain. Drop-in replacement for openai.api_base — no application code changes required.

Monitoring & Alerting

Prometheus + Grafana dashboards for GPU utilization, request latency (p50/p95/p99), queue depth, KV cache, and error rate.

Security & Data Privacy

Single-tenant bare metal. Your inference requests never touch ZenoCloud logging. LUKS encryption at rest. DPA available on request.

Auto-Restart & Scaling

systemd restarts vLLM on crash within 10 seconds. Horizontal scaling via nginx load balancer when concurrency grows beyond single GPU.

GPU Hardware Available

All GPUs are in India datacenter (Mumbai). Per node, per month, 1-month minimum. Managed ops add-on: ₹15,000 ($179) per node/month.

H100 80GB
VRAM 80GB HBM3
Best For 70B+ training, high-throughput inference, clusters
Per Node / Month ₹1,80,000$2,099 ≈ ₹247/hr effective≈ ₹247/hr effective
H200 141GB
VRAM 141GB HBM3e
Best For 405B-class models, DeepSeek V3, multi-GPU clusters
Per Node / Month ₹2,50,000$2,799 ≈ ₹342/hr effective≈ ₹342/hr effective
A100 80GB
VRAM 80GB HBM2e
Best For 70B inference (FP16), Llama 3.1 70B, Mixtral
Per Node / Month ₹97,000$1,099 ≈ ₹133/hr effective≈ ₹133/hr effective
B200
VRAM 192GB HBM3e
Best For Frontier-scale training, largest open models
Per Node / Month ₹3,95,000$4,499 ≈ ₹541/hr effective≈ ₹541/hr effective
RTX PRO 6000
VRAM 96GB GDDR7
Best For Image/video generation, quantized 70B inference
Per Node / Month ₹1,10,000$1,249 ≈ ₹151/hr effective≈ ₹151/hr effective
L40S
VRAM 48GB GDDR6
Best For 13B models, Stable Diffusion XL, image gen
Per Node / Month ₹55,000$599 ≈ ₹75/hr effective≈ ₹75/hr effective
AMD MI300X
VRAM 192GB HBM3
Best For Large-memory inference; MI325X also available
Per Node / Month On request

* Monthly commitment, 1-month minimum — no hourly product; effective ₹/hr (monthly ÷ 730) shown for comparison only. Managed ops add-on: ₹15,000 ($179) per node/month. More configurations available on request. Multi-node NVLink clusters on custom pricing.

Pricing

Managed AI Infrastructure Packages

Per node, per month, 1-month minimum. Pricing includes GPU, OS, runtime, and monitoring. Managed ops add-on: ₹15,000 ($179) per node/month.

Starter
/node/mo

For indie builders, POC stage, and image/video generation workloads

  • L40S 48GB (₹55,000 / $599 per mo)
  • RTX PRO 6000 96GB (₹1,10,000 / $1,249 per mo)
  • vLLM or Ollama runtime deployment
  • OpenAI-compatible API endpoint
  • Basic Grafana monitoring dashboard
  • trial node before you commit
Talk to an Engineer
Most Popular
Growth
/node/mo

For funded startups and production AI workloads

  • A100 80GB (₹97,000 / $1,099 per mo)
  • H100 80GB (₹1,80,000 / $2,099 per mo)
  • Auto-scaling + nginx load balancing
  • Full Prometheus + Grafana monitoring with alerting
  • Model optimization: quantization, batching
  • Slack/email support + onboarding call
Talk to an Engineer
Scale
/node/mo

For Series A+ teams with heavy inference or training workloads

  • H200 141GB (₹2,50,000 / $2,799 per mo)
  • B200 192GB (₹3,95,000 / $4,499 per mo)
  • Multi-node NVLink fabric on request
  • Custom SLA + dedicated ML ops engineer
  • 24/7 named engineer, 15-min P1 response
  • Quarterly architecture reviews
Scope a Custom Plan

Monthly commitment, 1-month minimum. Managed ops add-on ₹15,000 ($179) per node/mo. AMD MI300X and more configurations on request.

Managed GPU vs Raw GPU Rental

RunPod and Lambda Labs give you a server. ZenoCloud gives you a running, managed production deployment — in an India datacenter.

RunPod / Lambda Labs
ZenoCloud Managed
GPU hardware provisioning
OS + CUDA stack setup
vLLM / runtime installation
Model download and configuration
OpenAI-compatible API endpoint
Grafana monitoring dashboard
24/7 ops team (crash recovery)
Horizontal scaling support
India DC (DPDP compliance)
INR billing, no FX risk
Self-serve control panel
See Managed Pricing
FAQ

Frequently Asked Questions

How much does it cost to self-host an LLM in India?
A 13B-class model on an L40S node costs ₹55,000/month ($599). A 70B model (Llama 3.1 70B) on A100 80GB costs ₹97,000/month ($1,099), or ₹1,80,000/month ($2,099) on H100 for higher throughput. H200 nodes for 405B-class models are ₹2,50,000/month ($2,799). All prices are per node per month with a 1-month minimum; managed ops is a ₹15,000 ($179) add-on per node.
What is the difference between managed GPU and raw GPU rental?
Raw GPU rental (RunPod, Lambda Labs) gives you root access to a server. You install CUDA, download your model, configure vLLM, set up monitoring, and handle incidents. ZenoCloud managed GPU includes all of that plus 24/7 ops, automated crash recovery, and an OpenAI-compatible API endpoint ready in 2–7 days.
Which GPU should I choose for my model?
7B–13B models and image generation: L40S. 70B models quantized to 4-bit: RTX PRO 6000 or A100 80GB. 70B models (FP16): A100 80GB or H100. 405B+ models (Llama 3.1 405B, DeepSeek V3): H200 or B200 NVLink cluster. We recommend the right GPU after a 15-minute scoping call based on your concurrency and budget.
Does ZenoCloud satisfy DPDP Act 2023 data localization requirements?
Yes. All inference runs at our Mumbai location within Indian jurisdiction. Your inference payloads and responses stay on your GPU server — we collect only infrastructure metrics (GPU utilization, container health). We sign a Data Processing Agreement confirming no data is used for training or leaves India.
How long does GPU provisioning take?
Single-GPU setups (L40S, RTX PRO 6000) are ready in 2–3 business days. A100 single-node takes 3–5 days. H100, H200, and B200 multi-node NVLink clusters take 5–7 days. We confirm lead time during the scoping call.
Can I bring my own fine-tuned model or HuggingFace checkpoint?
Yes. Provide a HuggingFace Hub repo URL (public or private with read token), an S3-compatible bucket URL, or a local .safetensors checkpoint. We upload the model to your NVMe storage, encrypted at rest with LUKS, and configure vLLM. LoRA / PEFT adapters are merged or applied at runtime.
Can I evaluate a node before the monthly term begins?
Yes. We provision a trial node so you can validate your model on the exact hardware before the monthly term begins. Nearly every client evaluates on a trial node first. Request a trial and our engineering team scopes the right deployment for your model.
Pre-revenue, real infrastructure

Deploy Your First LLM in 5 Business Days

Tell us your model, concurrency requirements, and compliance needs. We'll scope a deployment plan and can provision a trial node before you commit.

Talk to a GPU Engineer Request a Quote
+1 714 242 5683 · +91 99991 08033 · support@zenocloud.io

Explore AI Infrastructure

Sub-products within the AI / GPU pillar — from inference and LLM hosting to model training and raw GPU hardware.

LLM Hosting

Self-host Llama, Mistral, DeepSeek on dedicated GPUs

AI Inference Hosting

vLLM, TGI, Triton — production inference at scale

AI Model Training

Fine-tuning and training on A100 / H100 clusters

ML Infrastructure

Full ML ops stack: storage, scheduling, monitoring

GPU Hosting Catalog

L40S to B200-class — specs and pricing

H100 GPU Servers

NVIDIA H100 80GB SXM — specs and availability