Deploy Open-Source LLMs (Llama 3 & Mistral) on a Dedicated GPU Server

If you're building generative AI applications, transitioning from third-party APIs to self-hosted open-weight models (like Llama 3.1 or Mistral) is a massive leap forward for data privacy and cost control at scale.

However, getting the MLOps right—managing CUDA drivers, VRAM allocation, and high-concurrency serving—can be a headache.

At Leo Servers, we provide bare-metal GPU servers pre-configured for AI. To help our users, we've published a comprehensive, production-ready walkthrough.

What the Tutorial Covers

We break down three distinct deployment strategies:

1. Ollama: The fastest path to getting an OpenAI-compatible REST API running in under 5 minutes.
2. vLLM: The industry standard for high-throughput production. We show you how to implement PagedAttention for continuous batching.
3. HuggingFace Transformers: For custom pipelines and fine-tuning.

Sneak Peek: Real Benchmarks

We ran these tests on a single LeoServers RTX 4090 (24 GB) instance. Notice how 4-bit quantization actually improves throughput due to memory bandwidth efficiency:

Model	Quantization	Tokens/sec	VRAM used
Mistral 7B Instruct	FP16	78 t/s	14.1 GB
Mistral 7B Instruct	AWQ 4-bit	94 t/s	4.8 GB

Production Readiness

The guide doesn't stop at just running the model. We also provide the exact configuration files to:

• Run your vLLM instance as a persistent systemd service.
• Secure your port 8000 endpoint using an Nginx reverse proxy with Let's Encrypt SSL and API key header validation.

For read more and to grab all the bash commands and Python snippets, visit the tutorial link: [https://www.leoservers.com/tutorials/howto/setup-llm-server/]