ML Infrastructure

Managed GPU Infrastructure for ML Teams

GPU clusters, shared storage, and MLOps tooling — all managed by ZenoCloud. A100 and H100 in India via E2E Networks. You run experiments. We handle the hardware, CUDA, and ops.

Talk to an Engineer See GPU Hardware

A100 and H100 in India NVLink fabric available 17 years infra ops since 2009 5,000 INR free trial credits

Running production workloads for

500+

GPUs Available via E2E Networks

3–5x

Cheaper Than US GPU Clouds

7 GB/s

Local NVMe Read Throughput

24/7

Hardware Monitoring & Ops

2–7 days

Provisioning Lead Time

What ZenoCloud Manages

The full ML infrastructure stack — not just a GPU server. No CUDA driver debugging, no hardware babysitting, no 2 AM disk failures interrupting training runs.

GPU Compute

A100 40GB/80GB and H100 80GB available single-node and multi-node. NVLink fabric for intra-node GPU communication. Up to 8 GPUs per node.

Storage Architecture

Local NVMe (>7 GB/s) for active dataset loading. NFS shared storage for multi-node dataset access. Object storage integration for checkpoint persistence.

CUDA + Framework Stack

Ubuntu 22.04, CUDA 12.4, cuDNN 9.0, NCCL 2.19 pre-installed and validated. PyTorch, TensorFlow, and JAX available as base environments.

Monitoring & Alerting

GPU utilization, training loss, memory pressure, disk throughput — all monitored. Alerts fire if GPU fails mid-training so you can checkpoint and recover.

MLOps Integration

Weights & Biases, MLflow, DVC, and Neptune work out of the box. GitHub Actions can trigger training runs via SSH or REST. No special client library needed.

Multi-Tenant Team Access

SSH access with per-user credentials. Job isolation via SLURM scheduling for shared clusters. Private VPC networking — your nodes are not accessible to other tenants.

GPU Hardware for ML Workloads

Matched to your workload: fine-tuning, distributed training, inference. All nodes are in India datacenter (Mumbai via E2E Networks).

GPU	VRAM	FP16 TFLOPS	ML Use Case	Reserved / Month
L4	24GB GDDR6	120	LoRA fine-tuning 7B, embedding training, dev/test	₹30,000 ($360)
L40S	48GB GDDR6	362	Full fine-tune 7B, LoRA 13B, diffusion model training	₹75,000 ($900)
A100 40GB	40GB HBM2e	312	Full fine-tune 13B, LoRA 70B, medium training runs	Contact for pricing
A100 80GB	80GB HBM2e	312	Full fine-tune 70B (FSDP), continued pre-training	₹1,50,000 ($1,800)
H100 SXM	80GB HBM3	989	Large-scale training, 70B+ models, 2x A100 throughput	₹1,50,000 ($1,800)
H200 SXM	141GB HBM3e	989	405B+ models, multi-node clusters, frontier training	₹2,00,000 ($2,400)

VRAM 24GB GDDR6

FP16 TFLOPS 120

ML Use Case LoRA fine-tuning 7B, embedding training, dev/test

Reserved / Month ₹30,000 ($360)

L40S

VRAM 48GB GDDR6

FP16 TFLOPS 362

ML Use Case Full fine-tune 7B, LoRA 13B, diffusion model training

Reserved / Month ₹75,000 ($900)

A100 40GB

VRAM 40GB HBM2e

FP16 TFLOPS 312

ML Use Case Full fine-tune 13B, LoRA 70B, medium training runs

Reserved / Month Contact for pricing

A100 80GB

VRAM 80GB HBM2e

FP16 TFLOPS 312

ML Use Case Full fine-tune 70B (FSDP), continued pre-training

Reserved / Month ₹1,50,000 ($1,800)

H100 SXM

VRAM 80GB HBM3

FP16 TFLOPS 989

ML Use Case Large-scale training, 70B+ models, 2x A100 throughput

Reserved / Month ₹1,50,000 ($1,800)

H200 SXM

VRAM 141GB HBM3e

FP16 TFLOPS 989

ML Use Case 405B+ models, multi-node clusters, frontier training

Reserved / Month ₹2,00,000 ($2,400)

* Reserved 3-month pricing shown. On-demand +25%. Multi-GPU cluster pricing is custom — contact for NVLink and multi-node InfiniBand scoping.

Pricing

ML Infrastructure Packages

Monthly reserved pricing — more predictable than on-demand for sustained ML workloads. Includes hardware, OS, storage, and 24/7 ops.

Starter

/month

For small ML teams, LoRA fine-tuning, and experiment phases

Single GPU: L4 (24GB) or L40S (48GB)
100GB local NVMe storage
PyTorch + TensorFlow pre-installed
SSH access with monitoring dashboard
Email support + documentation
5,000 INR free trial credits

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Managed ML Infrastructure vs Self-Managed GPU Rental

The alternative to managed ML infra is a DevOps hire at 30–50L/year or weeks of your engineers debugging CUDA drivers. Neither is a good trade.

Feature	Raw GPU Rental (RunPod / Lambda)	ZenoCloud Managed ML Infra
GPU hardware provisioning
OS + CUDA + cuDNN install
ML framework pre-installation
NVLink / NCCL configuration
Shared NFS storage for multi-node
SLURM job scheduling
24/7 hardware monitoring + replacement
W&B / MLflow integration support
India DC (DPDP compliance)
Self-serve control panel

Raw GPU Rental (RunPod / Lambda)

ZenoCloud Managed ML Infra

GPU hardware provisioning

OS + CUDA + cuDNN install

ML framework pre-installation

NVLink / NCCL configuration

Shared NFS storage for multi-node

SLURM job scheduling

24/7 hardware monitoring + replacement

W&B / MLflow integration support

India DC (DPDP compliance)

Self-serve control panel

Talk to an Engineer

FAQ

Frequently Asked Questions

What is the difference between ML infrastructure and LLM hosting?

LLM hosting focuses on deploying an inference endpoint for a specific language model. ML infrastructure is broader — it covers the full stack needed by an ML team: GPU compute, shared storage, job scheduling, distributed training, experiment tracking integration, and ops tooling. If you're running training runs, managing datasets, and have multiple researchers sharing resources, you need ML infrastructure, not just an inference endpoint.

Does ZenoCloud support distributed training across multiple GPUs?

Yes. Single-node multi-GPU training with NVLink is available on A100 and H100 nodes (up to 8 GPUs per node). We pre-configure NCCL for intra-node GPU communication and tune NUMA settings for optimal memory bandwidth. Multi-node distributed training is available on scoping — contact us with your model size and target parallelism strategy (DDP, FSDP, DeepSpeed ZeRO).

What MLOps tools does ZenoCloud integrate with?

Weights & Biases, MLflow, Neptune, and DVC work out of the box — you configure the API key and logging endpoint in your training script. ZenoCloud doesn't require a proprietary SDK. GitHub Actions can trigger training runs via SSH. For experiment tracking, we recommend W&B for teams already using it and MLflow for self-hosted tracking within your VPC.

How should I structure storage for ML training?

Use local NVMe for your active training dataset (fastest random read for DataLoader throughput). Use NFS shared storage for datasets shared across multiple GPU nodes. Use object storage (S3-compatible) for checkpoint archival and model weights. We provision this storage architecture for Growth and Scale tier clients. Starter tier includes local NVMe only — NFS is an add-on.

Can I run SLURM on ZenoCloud ML infrastructure?

Yes. SLURM workload manager is available on Growth and Scale tier for job queuing, resource allocation, and preventing GPU idle time between runs. For Starter tier single-GPU setups, SLURM is overkill — direct SSH and screen/tmux session management works fine. We configure SLURM with sensible defaults for ML workloads; you submit jobs with standard sbatch scripts.

What is the GPU pricing for ML infrastructure in India?

Reserved monthly pricing: L4 (24GB) at ₹30,000/mo ($360), L40S (48GB) at ₹75,000/mo ($900), A100 80GB at ₹1,50,000/mo ($1,800), H100 SXM at ₹1,50,000/mo ($1,800). On-demand pricing is 25% higher. Multi-GPU and multi-node clusters are custom priced. All prices include hardware, power, bandwidth, OS, CUDA stack, and 24/7 ops — not just GPU rental.

What happens if a GPU fails during a long training run?

Our Bangalore NOC monitors all GPU nodes 24/7 with hardware health checks running every 60 seconds. If a GPU fails mid-run, we alert immediately and prioritize hardware replacement or node migration. We recommend configuring checkpoint saves every N steps (typically every 500–1000 steps for long runs) so training can resume from the last checkpoint with minimal loss. We provide a pre-configured checkpoint callback for PyTorch Lightning and HuggingFace Trainer on request.

Reserve capacity, not just compute

Build Your ML Infrastructure in India

Tell us your model size, team size, and training frequency. We scope the right GPU configuration, storage, and scheduling setup. 5,000 INR in free trial credits to get started.

Talk to an Engineer Claim ₹5,000 GPU Credit

+1 714 242 5683 · +91 99991 08033 · support@zenocloud.io

Related AI Services

Other products in the ZenoCloud AI / GPU pillar.