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Solutions · GPUaaS · AI Training · AI Inference

GPU Cloud

Deploy on-premise with complimentary installation and maintain full control over your AI training workflows.

Distributed trainingHyperparameter tuningFine-tuningMonitoringDeployment
01 — Training capabilities

Train, fine-tune, optimize.

Comprehensive tools to train, fine-tune and optimize your AI models with ease.

Advanced training tools

Our platform provides cutting-edge training infrastructure for various AI models, from language models to computer vision systems.

  • Distributed training across multiple GPUs
  • Automatic hyperparameter optimization
  • Real-time training metrics and visualization
  • Custom loss functions and training loops
Model fine-tuning options

Fine-tune pre-trained models with your custom data to achieve better performance for your specific use cases.

  • Low-shot and few-shot learning capabilities
  • Parameter-efficient fine-tuning methods
  • Domain adaptation tools
  • Model quantization and optimization
02 — Training process

Four steps to a trained model.

Four easy steps to train your AI models effectively.

  1. 01

    Data Preparation

    Upload and preprocess your training data with our intuitive data management tools.

  2. 02

    Model Selection

    Choose from our library of pre-trained models or import your own architecture.

  3. 03

    Configuration

    Set hyperparameters and training settings with our guided configuration interface.

  4. 04

    Training & Monitoring

    Start training and monitor progress with real-time metrics and visualizations.

03 — Deployment

Deploy in four steps.

A streamlined AI model deployment process.

01 — Log in to dashboard

Access your AI platform management interface using your enterprise credentials.

  • Enterprise SSD Login
  • Permission Management
  • Multi-tenant Support
  • 2FA Authentication
  • Session Management
02 — Install AI models

Browse and install the required AI model packages from our application marketplace.

  • One-click Installation
  • Version Management
  • Dependency Check
  • Compatibility Testing
  • Rollback Support
03 — Upload training data

Upload your enterprise data and initiate model fine-tuning.

  • LoRA Fine-tuning
  • SFT Training
  • Progress Monitoring
  • Data Validation
  • Hyperparameter Tuning
04 — Deploy application

Deploy your trained models as enterprise internal applications.

  • API Service
  • Containerized Deployment
  • Load Balancing
  • Auto-scaling
  • Health Monitoring
04 — Path

From data to a served model.

The published path a workload takes through the platform.

Fig. 01 — Training and deployment path
TRAINING DATAPDF · TXT · CSV
PRE-TRAINED MODELSplatform marketplace
DISTRIBUTED TRAININGmultiple GPUs
FINE-TUNINGLoRA · SFT · RAG
CHECKPOINTScompare runs
API SERVICEenterprise applications
CONTAINERIZEDdeployment
LOAD BALANCINGauto-scaling
HEALTH MONITORINGreal-time metrics

Methods and deployment targets shown are those published in the platform's training and deployment workflow.

Request a Custom Deployment Consultation

Our technical specialists will develop a tailored implementation plan with comprehensive support