Hosted a large LLM on 4× H200s , now offering help with private LLM deployments

Recently worked on deploying a large language model on a server with:

• 4× NVIDIA H200 GPUs
• ~3 TB RAM
• vLLM for inference
• OpenAI-compatible API
• Docker-based deployment

The interesting part wasn't just getting the model to run. We had to deal with GPU allocation, model serving configuration, memory/KV-cache considerations, networking, and making the deployment usable as an actual inference service.

I'm going deeper into private/local LLM infrastructure helping teams run models on their own GPU servers instead of relying entirely on external APIs.

If you're currently trying to:

• self-host an LLM
• deploy vLLM
• use your own GPU server
• build a private RAG system
• expose an internal LLM API
• optimize inference

yacineyacine520@gmail.com
feel free to Email me . Happy to take a look at what you're building and see if I can help.