TGRP
AI Infrastructure

Core Technology Framework

TGRP has built a three-layer vertical architecture spanning from the hardware foundation to the application layer. Leveraging four core technological strengths, TGRP ensures the ultimate optimization of AI infrastructure performance.

Application Layer

Directly addresses industry pain points, delivering scenario-based AI capabilities.

  • Vertical industry models
  • Digital twin interfaces
  • Automated decision-making systems
  • Multimodal interaction interfaces

Model Layer

Efficiently manages the full model lifecycle to achieve an optimal balance between performance and efficiency.

  • LLMOps automation framework
  • Distributed parallel training
  • Mixed-precision computing
  • Knowledge distillation and quantization

Compute Layer

Builds rock-solid foundational infrastructure to support ultra-large-scale parameter computing.

  • Heterogeneous compute acceleration
  • Ultra-low-latency wide-area networking
  • High-performance flash storage arrays
  • Intelligent thermal monitoring and control systems

Compute Layer Deep Dive: The Physical Foundation for Extreme Performance

Our compute layer is far more than a simple aggregation of servers. By introducing ultra-high-bandwidth, lossless RDMA networking and proprietary storage acceleration technologies, TGRP is able to push the linear scaling efficiency of ten-thousand-GPU clusters to over 95%.
Healthcare illustration

Three Core Technological Advantages

Building TGRP’s Technology Moat

Heterogeneous Compute Scheduling

Compatible with chips from all major vendors, enabling unified management and elastic, sliced scheduling across GPUs, NPUs, FPGAs, and other compute types.

Large-Scale Model Training Acceleration

Powered by a proprietary parallel computing framework, reducing communication overhead by 40% and improving compute efficiency by over 60% in ultra-large-parameter model training.

Multi-Scenario Deployment Adaptability

Supports coordinated deployment across cloud, edge, and endpoint environments, ensuring stable model operation under varying power consumption and latency constraints.