Solutions

Solution 01

Enterprise Private LLM Training Solution

Beta LLM Private Training Suite

One-stop private LLM training factory achieving complete data-to-application end-to-end pipeline with data security and autonomy.

Enterprise Private LLM Training Solution

Background

Large models have become the new infrastructure for various industries, but enterprises face two major challenges:

Data Security & Compliance

Core business and privacy data cannot be uploaded to public clouds, even desensitization may not be accepted by regulators.

Training Cost & Technical Barriers

Training demands extremely high compute, GPUs are expensive and scarce, lacking an engineering system from data to deployment.

Under this background, private training platforms deployed in enterprise data centers based on proprietary chips have become the strategic choice for leading institutions.

Solution Content

This is a 'one-stop private LLM training factory', the platform includes from bottom to top:

Computing Infrastructure

Dedicated training cluster based on proprietary training chips and high-density servers, supporting linear scaling from dozens to thousands of cards, deliverable by rack, cage, or zone.

Training Engineering

Unified training scheduling and management platform supporting PyTorch, DeepSpeed, Megatron, with built-in data cleaning, annotation, distributed training, mixed precision, and checkpoint recovery.

Model Operations

Provides evaluation and comparison tools, SFT/LoRA management, version rollback, canary release, and one-click deployment to inference clusters or API services, achieving 'data→model→application' end-to-end pipeline.

The entire solution is deployed in enterprise or partner networks, with clear data and model ownership, auditable processes, ensuring autonomy and compliance security.

Key Advantages

Let data speak — let results be visible

99.9%
System Availability

Enterprise-grade high availability

70%
Cost Reduction

Compared to public cloud GPU costs

10x
Training Acceleration

Distributed parallel optimization

100%
Data Autonomy

Full control with private deployment

Technical Capabilities

Deep technical capabilities powering your business

Security & Compliance

Data never leaves enterprise network, meeting requirements of highly regulated industries

High Performance

Based on proprietary training chips, supports thousand-card cluster linear scaling

Complete Toolchain

One-stop platform from data prep to deployment, significantly lowering technical barriers

Continuous Evolution

Supports model versioning, A/B testing, canary release for business continuity

Target Industries

This solution is designed for the following industries and use cases

Finance
Government
Manufacturing
Healthcare
Energy
Telecom
Research

Implementation Process

Expert-guided implementation ensuring seamless project delivery

01
1-2 weeks
Requirement Analysis

Deep dive into business scenarios, data scale, computing needs and compliance requirements

02
2-3 weeks
Solution Design

Design hardware configuration, network topology, storage architecture and software platform

03
3-4 weeks
Environment Setup

Deploy hardware, configure network, install training platform software

04
2-3 weeks
POC Validation

Training tests with real data to validate performance and effectiveness

05
1-2 weeks
Training & Delivery

Team training, documentation delivery, ongoing technical support

FAQ

Common questions answered

Main advantages: 1) Data security - core data never leaves enterprise network; 2) Cost control - lower long-term costs; 3) Autonomy - not restricted by third-party services; 4) Customization - deep optimization for industry characteristics.

Depending on business needs, minimum configuration starts from 8 cards for small-scale fine-tuning, standard configuration recommends 64-128 cards for industry model training, large-scale scenarios can scale to thousands of cards. We provide reasonable configuration recommendations based on your specific needs.

Full support for mainstream frameworks including PyTorch, TensorFlow, DeepSpeed, Megatron-LM, with deeply optimized versions for proprietary chips ensuring best performance. Also supports HuggingFace ecosystem for seamless use of open-source models.

Standard projects take approximately 9-14 weeks from requirement analysis to official delivery. Specific time depends on cluster scale, data center conditions and customization needs. We offer fast deployment solutions that can be shortened to 6-8 weeks with existing data center infrastructure.

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