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.
Scale from 1 GPU to multi-node clusters in seconds. No capacity planning or provisioning delays.
Pay only for what you use. No reserved instances, no minimum commitments, no hidden fees.
Billing starts when your pod launches and stops the moment you terminate it. Down to the second.
PyTorch, TensorFlow, JAX, and DeepSpeed come pre-installed. Start training immediately.
NVMe SSDs with up to 7 GB/s throughput. No bottleneck between storage and GPU memory.
Pay up to 55% less than AWS, GCP, or Azure for the same GPU hardware and performance.
# Option 1: Use the AI Inference API (OpenAI-compatible)
from openai import OpenAI
client = OpenAI(
base_url="https://api.voltagegpu.com/v1",
api_key="YOUR_VOLTAGEGPU_API_KEY"
)
response = client.chat.completions.create(
model="deepseek-ai/DeepSeek-R1",
messages=[{"role": "user", "content": "Explain transformers"}],
max_tokens=1024
)
print(response.choices[0].message.content)
# Option 2: Managed Fine-Tuning via API
import requests
job = requests.post(
"https://voltagegpu.com/api/volt/fine-tuning",
headers={"X-API-Key": "volt_xxx"},
json={
"name": "my-llama-finetune",
"taskType": "text",
"model": "meta-llama/Llama-3.1-8B-Instruct",
"dsRepo": "myorg/training-data",
"hours": 2,
"fieldInstruction": "instruction",
"fieldOutput": "output"
}
).json()
print(f"Training job: {job['job']['id']}")$5 free credit. No credit card required.