GPU Cloud India

GPU Hosting with Managed Ops Included

H100, A100, L40S, and L4 GPUs in our Mumbai datacenter. We provision the hardware, configure CUDA and vLLM, and keep everything running 24/7. You focus on your model. INR billing, no forex risk, DPDP-compliant.

Talk to a GPU Engineer See GPU Pricing
500+ GPUs via E2E Networks India DC — Mumbai DPDP From ₹49/hr on-demand 17 years infra ops since 2009 INR billing — no forex risk
Running production workloads for
Revolt MotorsPC JewellerRR KabelImpresarioIntentwiseLoomBhimaBGaussMitutoyo
500+
GPUs in India DC
3–5x
Cheaper Than US GPU Clouds
₹49 /hr
Starting Price (L4)
24/7
Managed Ops & NOC
<15 min
P1 Incident Response

What ZenoCloud GPU Hosting includes

Raw GPU rental gives you a box. ZenoCloud gives you a running, managed deployment — from bare metal to a live API endpoint.

H100, A100, L40S, L4 GPUs

Four GPU classes covering every workload — from 7B dev inference on L4 (₹49/hr) to 70B+ training on H100 SXM (₹249/hr). All hardware in Mumbai, benchmarked before handoff.

Runtime installed for you

vLLM, Ollama, TGI, or TorchServe configured for your model family. CUDA 12.4, cuDNN 9.0, and NCCL all handled. Bring your HuggingFace checkpoint or S3 URL.

OpenAI-compatible API endpoint

Drop-in replacement for openai.api_base — HTTPS endpoint at your subdomain, ready in 2–7 days. No application code changes required to switch from OpenAI.

Grafana monitoring + alerting

Prometheus + Grafana dashboards: GPU utilization, p50/p95/p99 latency, queue depth, KV cache, error rate. Alerts go to our NOC, not your inbox.

Single-tenant, DPDP-compliant

Bare metal, single-tenant. Inference payloads never touch ZenoCloud logs. LUKS encryption at rest. DPA available. Mumbai jurisdiction satisfies DPDP Act 2023.

Auto-restart + horizontal scale

systemd restarts vLLM on crash within 10 seconds. Nginx load balancer handles horizontal scaling as concurrency grows. No manual intervention needed.

GPU hardware available — India DC

All GPUs racked in Mumbai via E2E Networks. Managed pricing includes hardware, OS, runtime, monitoring, and 24/7 ops — not just GPU rental.

NVIDIA L4
VRAM 24GB GDDR6
Best For 7B models (FP16), embeddings, dev/test
On-Demand ₹49/hr ($0.75)
Reserved /mo ₹30,000 ($360)
NVIDIA L40S
VRAM 48GB GDDR6
Best For 13B models, Stable Diffusion XL, image gen
On-Demand ₹150/hr ($2.25)
Reserved /mo ₹75,000 ($900)
NVIDIA A100 80GB
VRAM 80GB HBM2e
Best For 70B inference (FP16), Llama 3.1 70B, Mixtral
On-Demand ₹220/hr ($3.30)
Reserved /mo ₹1,50,000 ($1,800)
NVIDIA H100 SXM
VRAM 80GB HBM3
Best For 70B+ training, 2x A100 throughput, clusters
On-Demand ₹249/hr ($3.75)
Reserved /mo ₹1,50,000 ($1,800)
NVIDIA H200 SXM
VRAM 141GB HBM3e
Best For 405B+ models, DeepSeek V3, multi-GPU
On-Demand ₹300/hr ($4.50)
Reserved /mo ₹2,00,000 ($2,400)

* Reserved 3-month pricing. On-demand +25% vs reserved. Multi-node NVLink clusters on custom pricing. Contact for A100 40GB and RTX 4090 configs.

Pricing

GPU pricing — hourly or reserved

Managed pricing includes GPU, OS, runtime, monitoring, and 24/7 ops. Not raw GPU rental — a deployed, running system. Switch between on-demand and reserved as your workload stabilizes.

L4 / L40S
/hour

Dev, test, 7B–13B inference, image generation

  • L4 24GB (₹49/hr) or L40S 48GB (₹150/hr)
  • vLLM or Ollama runtime configured
  • OpenAI-compatible API endpoint
  • Grafana monitoring dashboard
  • Email support + documentation
  • Reserved monthly rate: ₹30k–₹75k
Start Free Trial
Most Popular
A100
/hour

70B inference, Llama 3.1, Mixtral, multi-tenant APIs

  • A100 40GB (₹170/hr) or 80GB (₹220/hr)
  • Auto-scaling + nginx load balancing
  • Full Prometheus + Grafana + alerting
  • Model optimization: quantization, batching
  • Slack/email support + onboarding call
  • Reserved monthly rate: ₹1,00k–₹1,50k
Talk to an Engineer
H100 / H200
/hour

70B+ training, 405B serving, multi-node clusters

  • H100 SXM 80GB or H200 SXM 141GB
  • NVLink multi-node clusters on request
  • Custom SLA + dedicated ML ops engineer
  • Fine-tuning and training pipelines
  • 24/7 named engineer, 15-min P1 response
  • Reserved monthly rate: ₹1,50k–₹2,00k
Scope a Custom Plan

All tiers include 5,000 INR free trial credits. 3-month reserved saves ~25% vs on-demand. H100/H200 multi-node NVLink clusters on custom pricing.

ZenoCloud GPU vs RunPod / Lambda Labs

RunPod and Lambda Labs give you a server. ZenoCloud gives you a running managed deployment in an India datacenter — with INR billing and a team who handles the ops.

RunPod / Lambda Labs (US)
ZenoCloud (India, Managed)
GPU hardware provisioning
OS + CUDA stack setup
vLLM / runtime configured
Model download + configuration
OpenAI-compatible API endpoint
Grafana monitoring dashboard
24/7 ops (crash recovery, NOC)
India DC (DPDP compliance)
INR billing — no FX risk
15-min P1 incident response
Self-serve control panel
See Managed GPU Pricing
FAQ

GPU hosting — common questions

Which GPU should I pick for my model?
7B models (FP16): L4 or RTX 4090. 13B models: L40S or A100 40GB. 70B models FP16: A100 80GB or H100. 70B quantized to 4-bit: A100 40GB. 405B+ (Llama 3.1 405B, DeepSeek V3): H100 or H200 NVLink cluster. We recommend the right GPU in a free 15-minute scoping call based on your concurrency and budget.
How is ZenoCloud different from RunPod or Lambda Labs?
RunPod and Lambda Labs give you root access to a GPU server — you install CUDA, configure vLLM, set up monitoring, and handle incidents yourself. ZenoCloud includes all of that setup plus 24/7 managed ops, automated crash recovery, and an OpenAI-compatible API endpoint ready in 2–7 days. And we're in India — DPDP-compliant, INR billing, no forex exposure.
How long does GPU provisioning take?
Single-GPU setups (L4, L40S) are ready in 2–3 business days. A100 single-node takes 3–5 days. H100 / H200 multi-node NVLink clusters take 5–7 days. We confirm lead time during the scoping call and keep you updated throughout.
Does this satisfy DPDP Act 2023 data localization requirements?
Yes. All inference runs in our Mumbai datacenter within Indian jurisdiction. 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.
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 local .safetensors checkpoints. We upload the model to your NVMe storage, encrypted at rest with LUKS, and configure vLLM. LoRA and PEFT adapters are merged or applied at runtime.
Is there a free trial?
Yes — 5,000 INR in free GPU credits, no credit card required. Credits cover roughly 100 hours on an L4 GPU or about 20 hours on an A100. Talk to a GPU engineer to activate your trial and scope the right deployment for your model.
What is the difference between on-demand and reserved pricing?
On-demand lets you spin up and shut down GPUs by the hour — ideal for experiments, training runs with variable schedules, or workloads that run <300 hours/month. Reserved (3-month commitment) saves about 25% and is right for production inference with steady traffic. You can start on-demand and convert to reserved once your workload is stable.
Why INR pricing instead of USD?
INR-denominated billing eliminates forex risk and makes budgeting predictable. No surprise costs from currency fluctuations between invoice and payment. Wire transfers and UPI both accepted. We also issue Indian GST invoices for input tax credit.
India's managed GPU cloud

Deploy your first model in 5 business days.

Tell us your model, concurrency requirements, and compliance needs. We scope a deployment plan and activate 5,000 INR in free trial credits to get you started.

Talk to a GPU Engineer Claim ₹5,000 GPU Credit
+1 714 242 5683 · +91 99991 08033 · support@zenocloud.io

GPU hardware by model

Each GPU page has detailed specs, VRAM requirements by model size, benchmark comparisons, and availability status.

H100 GPU Servers

80GB HBM3 — LLM training, 70B+ fine-tuning

H200 GPU Servers

141GB HBM3e — 405B+ models, DeepSeek V3

A100 GPU Servers

40GB / 80GB — 70B inference, Mixtral, general ML

L40S GPU Servers

48GB — 13B inference, image gen, video AI

L4 GPU Servers

24GB — 7B models, prototyping, budget inference

AI Infrastructure Pillar

LLM hosting, inference, training, ML ops