AI & Machine Learning Infrastructure

GPU-as-a-Service for AI & Machine Learning

Access enterprise-grade GPU infrastructure on-demand. Train LLMs, run ML workloads, and accelerate AI development with high-performance computing power tailored for deep learning and generative AI applications.

Deploy GPU ClusterView Pricing

AI & ML Use Cases

Power your AI initiatives with GPU infrastructure designed for modern machine learning workflows

LLM Training & Fine-Tuning

Train custom language models or fine-tune existing models like LLaMA, Mistral, or GPT for domain-specific applications.

  • Pre-training from scratch on massive datasets
  • Fine-tuning with LoRA, QLoRA, and full-parameter methods
  • RLHF (Reinforcement Learning from Human Feedback)
  • Distributed training across multi-GPU clusters
Real-Time AI Inference

Deploy production AI models with low-latency inference for chatbots, recommendation engines, and intelligent applications.

  • High-throughput batch inference for large datasets
  • Low-latency responses for interactive applications
  • TensorRT and ONNX optimization support
  • Auto-scaling based on request volume
Computer Vision & Image AI

Process images and video with deep learning models for object detection, segmentation, and generative applications.

  • Stable Diffusion, Midjourney-style image generation
  • YOLO, ResNet, EfficientNet for detection
  • Video processing and real-time streaming analytics
  • Medical imaging and diagnostic AI
Data Science & Analytics

Accelerate data processing pipelines, feature engineering, and model training for traditional ML workloads.

  • RAPIDS for GPU-accelerated data science
  • XGBoost, LightGBM gradient boosting on GPUs
  • PyTorch and TensorFlow ML pipeline acceleration
  • Time series forecasting and anomaly detection

Complete GPU Infrastructure

Everything you need to build, train, and deploy AI models at scale

Pre-Configured ML Environments

Launch instantly with PyTorch, TensorFlow, JAX, and popular frameworks pre-installed. CUDA, cuDNN, and NCCL ready to use.

High-Speed Interconnect

NVLink, NVSwitch, and InfiniBand connectivity for multi-GPU training. Optimized network topology for distributed workloads.

Flexible Storage Options

High-performance NVMe for training data, object storage for datasets, and shared file systems for distributed training.

On-Demand Scaling

Scale from single GPU instances to multi-node clusters. Pay only for what you use with per-minute billing.

Jupyter & VS Code Access

Browser-based development with JupyterLab, VS Code Server, or SSH access. No local setup required.

Model Versioning & MLOps

Integration with MLflow, Weights & Biases, and TensorBoard. Checkpoint management and experiment tracking.

Supported AI Frameworks

Work with your favorite tools and frameworks out of the box

PyTorch
TensorFlow
JAX
Hugging Face
LangChain
LlamaIndex
vLLM
DeepSpeed
ONNX Runtime
TensorRT
RAPIDS
Triton

FAQ

Common Questions About GPU-as-a-Service

What GPU types are available?

We offer NVIDIA H100, A100 (40GB/80GB), L40S, and other enterprise GPUs. Each GPU type is optimized for specific workloads - H100 for LLM training, A100 for general ML, and L40S for inference.

How does pricing work?

Pay-per-minute billing with no long-term commitments. Pricing varies by GPU type and configuration. Volume discounts available for sustained usage and reserved capacity.

Can I run distributed training?

Yes. Multi-GPU and multi-node training is fully supported with NVLink, NVSwitch, and InfiniBand connectivity. We support PyTorch DDP, Horovod, DeepSpeed, and other distributed frameworks.

What about data security and privacy?

Your data and models are completely isolated. We provide encrypted storage, private networking, and compliance with SOC 2, ISO 27001, and GDPR requirements. Option for dedicated bare-metal GPUs without multi-tenancy.

Ready to Accelerate Your AI Projects?

Start training models, running inference, or deploying AI applications with enterprise GPU infrastructure

Get Started with GPUsContact AI Specialist