Enterprise Private LLM Platform
Enterprise Private LLM Platform
Support Llama 4/Qwen 3.x/DeepSeek V4 and other mainstream open-source models with full-stack solutions for private deployment, fine-tuning, and inference acceleration
Core Technical Advantages
Core Technical Advantages
Multi-Model Support
Support Llama 4, Qwen 3.x, DeepSeek V4, GLM-5.x, Mistral and other mainstream open-source models with flexible switching
Inference Acceleration
vLLM continuous batching + PagedAttention + FlashAttention2 + quantization (INT8/INT4) — serve more concurrent requests on the same GPU memory
Efficient Fine-tuning Framework
Support LoRA/QLoRA/P-Tuning v2 — train only adapter weights instead of full-parameter updates, fine-tuning 70B-class models on a single GPU
Private & Secure Deployment
Support on-premise/private cloud/hybrid cloud deployment, data stays within internal network, compliant with MLPS 2.0/GDPR/HIPAA
Enterprise Application Scenarios
Enterprise Application Scenarios
01
Domain-Specific LLMs
Customized models for finance/healthcare/legal/manufacturing verticals, aligning domain terminology and output formats with your own corpus
- ·Domain Knowledge Injection (LoRA Fine-tuning)
- ·Professional Terminology Understanding
- ·Compliance Risk Control
- ·Continuous Iteration & Optimization
- ·Multi-language Support (CN/EN/JP/KR)
02
Intelligent Dialogue Assistant
Enterprise dialogue system with context memory, multi-turn conversations, intent recognition, <100ms response latency
- ·Multi-turn Dialogue Management (100+ turns)
- ·Long Context Understanding (128K tokens)
- ·Function Calling Tool Integration
- ·Streaming Output for Lower First-Token Latency
- ·Sentiment Analysis & Personalization
03
Code Generation Assistant
Support 40+ programming languages, generate code with repository context, and automatically produce runnable unit tests
- ·Code Completion & Generation
- ·Code Review & Optimization Suggestions
- ·Automated Unit Test Generation
- ·Bug Detection & Fixing
- ·Technical Documentation Auto-generation
Complete Deployment Process
Complete Deployment Process
Requirements & Solution Design
Evaluate business scenarios, data scale, performance requirements, recommend optimal model architecture (7B/13B/70B/400B)
Infrastructure Preparation
GPU server selection (A100/H100/Ascend 910), Kubernetes cluster setup, monitoring & alerting configuration
Model Deployment & Optimization
Model quantization (INT8/INT4), vLLM inference acceleration, multi-replica load balancing, TPS reaching 1000+
Data Preparation & Fine-tuning
Enterprise data cleaning & annotation, LoRA/QLoRA fine-tuning training, RLHF reinforcement learning
Testing & Evaluation
Functional testing, performance stress testing, security penetration testing, accuracy evaluation (BLEU/ROUGE/BERTScore)
Go-live & Operations Support
Gradual rollout, full launch, 7x24 monitoring, continuous model optimization, version management
Supported Models
支持的开源模型家族
FAQ
私有大模型常见问题
YGG 的私有大模型支持哪些模型？
支持主流开源大模型家族（Llama 4 / 3.x、Qwen 3.x、DeepSeek V4 / V3.2、GLM-5.x、Mistral、Gemma 4、Baichuan M-series），以及 Claude / GPT (OpenAI) / Gemini 商用 API。模型清单最后核对：2026-08-01。
模型微调通常需要多久？
典型 LoRA / QLoRA 微调 3-7 天完成；全量微调（Full Fine-Tune）约 2 周。时间取决于数据规模和目标任务。交付前提供离线评估报告和回滚方案。
推理性能如何？
用 vLLM 的连续批处理（continuous batching）+ PagedAttention，在同样显存预算下承载更高并发。配合 TensorRT 与 AWQ / GPTQ 量化压低显存占用，支持分布式推理与异构 GPU 调度。实际吞吐取决于模型规模、上下文长度和并发数，交付前在客户真实负载上压测给出实测值。
数据安全如何保障？
全流程 AES-256 静态加密 + TLS 1.3 传输加密；训练和推理均在客户边界内完成；审计日志全量留痕；符合等保 2.0 / ISO 27001 对齐要求。
支持哪些开源模型家族？
开源可私有部署家族：Llama 4 / 3.x、Qwen 3.x、DeepSeek V4 / V3.2、GLM-5.x、Mistral、Gemma 4、Baichuan M-series。每次社区发布新版会在 2 个月内完成接入评估与更新，模型清单带核对日期，可在私有大模型产品页查看。
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Free POC validation with professional technical consulting and deployment support