Advantages and technical requirements of running large language models like GPT, LLaMA, and Gemini on your own cloud infrastructure.
What Are Large Language Models?
Large Language Models (LLMs) are AI models trained on billions of parameters. They can perform tasks like text generation, summarization, translation, code writing, and Q&A with human-like fluency.
Why Run on Your Own Infrastructure?
- Data Privacy: Sensitive data never leaves your environment — full GDPR compliance
- Cost Control: API costs escalate quickly with heavy usage; self-hosted is more economical long-term
- Customization: Fine-tune models to your industry and proprietary data
- Latency: Millisecond-level response times with local inference
Popular Open-Source Models
LLaMA 3: Meta's high-performance open-source model family. Available in 8B, 70B, and 405B parameter variants.
Mistral/Mixtral: European-origin, efficient and fast models. Low resource consumption with MoE (Mixture of Experts) architecture.
Gemma: Google's lightweight and efficient open-source model. Can run even on edge devices.
Technical Requirements
- GPU: NVIDIA A100 (80GB) or H100 — single or multi-GPU depending on model size
- RAM: Minimum 64GB, 256GB+ system memory for large models
- Storage: NVMe SSD — high I/O for model weights and datasets
- Framework: Easy deployment with vLLM, TGI (Text Generation Inference), or Ollama
RAG (Retrieval-Augmented Generation)
RAG enables LLMs to access your up-to-date knowledge base for more accurate responses. Integrate with a vector database (pgvector, Weaviate) to build an internal knowledge assistant.
CloudRUX LLM Infrastructure
Size the infrastructure for LLaMA, Mistral or your custom models together with our engineers. Details of our GPU service will be announced at launch.