ML Infrastructure

Managed GPU Infrastructure for ML Teams

GPU clusters, shared storage, and MLOps tooling — all managed by ZenoCloud. A100 and H100 nodes in India, provisioned and operated by our engineers. You run experiments. We handle the hardware, CUDA, and ops.

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A100 and H100 in India NVLink fabric available 17 years infra ops since 2009 trial node before you commit

Running production workloads for

What ML infrastructure means

ML infrastructure: ML infrastructure is the combined GPU compute, shared storage, and MLOps tooling a machine learning team needs to run experiments, distinct from a single rented GPU. ZenoCloud's managed version pairs A100 and H100 GPUs in India with NFS shared storage and SLURM scheduling, and handles the hardware and CUDA layer so a team runs experiments without operating the cluster.
Managed GPU cluster: A managed GPU cluster is a group of GPU servers with shared storage and a job scheduler operated by the infrastructure provider rather than the ML team. ZenoCloud runs this with NVLink fabric available for multi-GPU jobs, backed by 17 years of infrastructure operations since 2009.

A100/H100

GPU Nodes Managed in India

2009

Running Infra Since

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).

GPU	VRAM	ML Use Case	Per Node / Month
L40S	48GB GDDR6	Full fine-tune 7B, LoRA 13B, diffusion model training	₹55,000$599 ≈ ₹75/hr effective≈ ₹75/hr effective
RTX PRO 6000	96GB GDDR7	LoRA 70B, image/video model training, quantized inference	₹1,10,000$1,249 ≈ ₹151/hr effective≈ ₹151/hr effective
A100 80GB	80GB HBM2e	Full fine-tune 70B (FSDP), continued pre-training	₹97,000$1,099 ≈ ₹133/hr effective≈ ₹133/hr effective
H100 80GB	80GB HBM3	Large-scale training, 70B+ models, 2x A100 throughput	₹1,80,000$2,099 ≈ ₹247/hr effective≈ ₹247/hr effective
H200 141GB	141GB HBM3e	405B+ models, multi-node clusters, long-context work	₹2,50,000$2,799 ≈ ₹342/hr effective≈ ₹342/hr effective
B200	192GB HBM3e	Frontier-scale training, largest open models	₹3,95,000$4,499 ≈ ₹541/hr effective≈ ₹541/hr effective

L40S

VRAM 48GB GDDR6

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

Per Node / Month ₹55,000$599 ≈ ₹75/hr effective≈ ₹75/hr effective

RTX PRO 6000

VRAM 96GB GDDR7

ML Use Case LoRA 70B, image/video model training, quantized inference

Per Node / Month ₹1,10,000$1,249 ≈ ₹151/hr effective≈ ₹151/hr effective

A100 80GB

VRAM 80GB HBM2e

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

Per Node / Month ₹97,000$1,099 ≈ ₹133/hr effective≈ ₹133/hr effective

H100 80GB

VRAM 80GB HBM3

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

Per Node / Month ₹1,80,000$2,099 ≈ ₹247/hr effective≈ ₹247/hr effective

H200 141GB

VRAM 141GB HBM3e

ML Use Case 405B+ models, multi-node clusters, long-context work

Per Node / Month ₹2,50,000$2,799 ≈ ₹342/hr effective≈ ₹342/hr effective

B200

VRAM 192GB HBM3e

ML Use Case Frontier-scale training, largest open models

Per Node / Month ₹3,95,000$4,499 ≈ ₹541/hr effective≈ ₹541/hr effective

* 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 (including AMD MI300X) on request. Multi-GPU NVLink and InfiniBand cluster pricing is custom.

Pricing

ML Infrastructure Packages

Per node, per month, 1-month minimum. Includes hardware, OS, and storage. Managed ops add-on: ₹15,000 ($179) per node/month.

Starter

/node/mo

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

L40S 48GB (₹55,000 / $599 per mo)
RTX PRO 6000 96GB (₹1,10,000 / $1,249 per mo)
100GB local NVMe storage
PyTorch + TensorFlow pre-installed
SSH access with monitoring dashboard
trial node before you commit

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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?

Per node, per month: L40S (48GB) at ₹55,000 ($599), RTX PRO 6000 (96GB) at ₹1,10,000 ($1,249), A100 80GB at ₹97,000 ($1,099), H100 at ₹1,80,000 ($2,099), H200 at ₹2,50,000 ($2,799), B200 at ₹3,95,000 ($4,499). Monthly commitment with a 1-month minimum — no hourly product. The managed ops add-on is ₹15,000 ($179) per node/month. Multi-GPU and multi-node clusters are custom priced.

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 — and can provision a trial node before you commit.

Talk to an Engineer Request a Quote

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

Related AI Services

Other products in the ZenoCloud AI / GPU pillar.