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GPU Cloud for AI Model Training

Train machine learning models on powerful cloud GPUs with per-second billing. No upfront costs, scale instantly from a single GPU to multi-node clusters.

VoltageGPU provides the most cost-effective GPU cloud infrastructure for training AI models. Whether you are training a transformer from scratch, running distributed training across multiple nodes, or iterating on research experiments, our cloud GPUs deliver the compute power you need at a fraction of the cost of traditional cloud providers. Access NVIDIA A100, H100, H200, and B200 GPUs on demand with no long-term commitments.

Key Benefits

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Scalable Compute

Scale from 1 GPU to multi-node clusters in seconds. No capacity planning or provisioning delays.

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No Upfront Cost

Pay only for what you use. No reserved instances, no minimum commitments, no hidden fees.

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Per-Second Billing

Billing starts when your pod launches and stops the moment you terminate it. Down to the second.

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Pre-installed Frameworks

PyTorch, TensorFlow, JAX, and DeepSpeed come pre-installed. Start training immediately.

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High-Speed Storage

NVMe SSDs with up to 7 GB/s throughput. No bottleneck between storage and GPU memory.

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85% Cost Savings

Pay up to 85% less than AWS, GCP, or Azure for the same GPU hardware and performance.

Recommended GPUs

NVIDIA A100 80GBView specs & pricing →NVIDIA H100 SXMView specs & pricing →NVIDIA H200View specs & pricing →NVIDIA B200View specs & pricing →

Recommended Models

DeepSeek R1Try this model →Llama 3.3 70BTry this model →Qwen 2.5 72BTry this model →

Code Example

Python
import requests

# Launch a training pod on VoltageGPU
response = requests.post(
    "https://api.voltagegpu.com/v1/pods/deploy",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    json={
        "gpu": "h100-sxm",
        "gpu_count": 4,
        "image": "pytorch/pytorch:2.3-cuda12.4-cudnn9-devel",
        "volume_size_gb": 200,
        "env": {
            "WANDB_API_KEY": "your-wandb-key",
            "HF_TOKEN": "your-hf-token"
        },
        "command": "torchrun --nproc_per_node=4 train.py \\
            --model llama-3.3-70b \\
            --dataset your-dataset \\
            --epochs 3 \\
            --batch_size 16 \\
            --learning_rate 2e-5"
    }
)

pod = response.json()
print(f"Training pod launched: {pod['id']}")
print(f"SSH: ssh root@{pod['ssh_host']} -p {pod['ssh_port']}")

Frequently Asked Questions

What GPUs are best for AI model training on VoltageGPU?
For large-scale training, we recommend the NVIDIA H100 SXM or H200 for their superior memory bandwidth and FP8 support. The A100 80GB remains an excellent choice for most training workloads. For budget-conscious training runs, the RTX 4090 offers outstanding price-performance for models that fit in 24GB VRAM.
How does per-second billing work for training jobs?
Billing begins the moment your GPU pod starts and stops when you terminate it. You are charged per second of GPU usage, so a 47-minute training run costs exactly 47 minutes of compute, not a full hour. This can save 10-20% compared to hourly billing providers.
Can I run distributed training across multiple GPUs?
Yes. You can deploy pods with up to 8 GPUs per node, all connected via NVLink for fast inter-GPU communication. For multi-node training, we support NCCL and RDMA over InfiniBand for minimal communication overhead.
Do I need to set up CUDA and PyTorch myself?
No. VoltageGPU provides pre-built container images with PyTorch, TensorFlow, JAX, and DeepSpeed already installed and optimized. Simply select your framework and start training immediately.
How much does AI training cost on VoltageGPU compared to AWS?
VoltageGPU is up to 85% cheaper than AWS for equivalent GPU hardware. An H100 on VoltageGPU starts at $2.49/h compared to $12-15/h on AWS. For a typical 100-hour training run on 4x H100s, you save over $4,000.

Explore Other Use Cases

🎯 Fine-Tune LLMs on Cloud GPUs🎨 AI Image Generation API🎬 AI Video Generation API🔍 Text Embeddings & RAG Pipeline🎙️ Speech to Text API - Whisper & MoreBrowse All ModelsBrowse All GPUs

Start Building Now

Deploy a GPU pod in under 60 seconds. $5 free credits, no credit card required.

Browse Available GPUs →Explore Models