AI on EL Deployment Overview

Deploying AI inference services on EL systems requires a complete stack from hardware drivers to the application layer. This page helps you understand the architecture and choose the right tool combination.

Deployment Stack

Hardware       GPU (NVIDIA)
    ↓
Drivers        NVIDIA Driver + CUDA Toolkit
    ↓
Runtime        Ollama / vLLM / llama.cpp
    ↓
Models         LLaMA / Qwen / Gemma / Mistral (GGUF/HF format)
    ↓
API Layer      OpenAI-compatible API (built-in)
    ↓
Frontend       Open WebUI / Custom apps
    ↓
Gateway        Nginx reverse proxy + SSL

Choosing a Solution

Solution	Best For	Difficulty	GPU Required
Ollama	Personal use, development	Easy	Optional
vLLM	Production inference, high concurrency	Medium	Required
llama.cpp	Maximum optimization, CPU inference, quantization	Medium	Optional
Open WebUI + Ollama	Team ChatGPT alternative	Easy	Optional
Stable Diffusion	Image generation	Medium	Required

Recommended Deployment Order

Quick Start (30 minutes)

Just want to run a local LLM quickly:

1. Install Ollama — one command install
2. ollama run llama3.1 — start chatting

Standard Deployment (2-4 hours)

Provide a usable AI service for your team:

1. NVIDIA Drivers & CUDA — install GPU drivers
2. Ollama — install and download models
3. Open WebUI — deploy web chat interface
4. Nginx Reverse Proxy — configure HTTPS and domain
5. Firewall — configure access control

Production Deployment

High-concurrency, low-latency inference service:

1. NVIDIA Drivers & CUDA — GPU drivers and CUDA
2. vLLM — high-performance inference engine
3. Nginx Reverse Proxy — load balancing and SSL
4. Monitoring — GPU and service monitoring
5. Performance Tuning — kernel and network optimization

EL-Specific Considerations

• SELinux: For containerized deployments, set setsebool -P container_use_devices on to allow GPU access
• Firewall: AI services listen on high ports (8080/11434 etc.) — open in firewalld or use Nginx reverse proxy
• systemd: Register Ollama/vLLM as systemd services for auto-start and crash recovery
• Storage: Large model files are typically 5-70 GB — ensure sufficient space in /var or model directory
• NVIDIA driver compatibility: On EL 9, use the NVIDIA official repo to avoid kernel version conflicts

Common issue: GPU not available

The most common post-deployment problem is “nvidia-smi works, but Ollama/vLLM can’t see the GPU and falls back to CPU.” Troubleshoot in this order:

1. Confirm the host sees the GPU

   nvidia-smi          # if no GPU is listed, the driver isn't installed — go back to the NVIDIA driver doc

2. Containerized deployment: check the NVIDIA Container Toolkit

   Skip this for bare-metal installs. If the GPU is invisible inside containers, nvidia-container-toolkit is usually missing or unconfigured:

   # Generate CDI device config after install (CDI is recommended for Podman)
   sudo nvidia-ctk cdi generate --output=/etc/cdi/nvidia.yaml
   # Mount the GPU at runtime
   podman run --rm --device nvidia.com/gpu=all nvidia/cuda:12.4.0-base-ubi9 nvidia-smi

3. SELinux blocking device access

   # Allow containers to access GPU device nodes under /dev
   sudo setsebool -P container_use_devices on
   # When troubleshooting, inspect AVC denials
   sudo ausearch -m avc -ts recent

4. CUDA / driver version mismatch

   If vLLM/PyTorch reports CUDA driver version is insufficient, the driver is older than the CUDA runtime the framework needs. The CUDA version shown in the top-right of nvidia-smi is the highest the driver supports and must be ≥ what the framework requires.

5. Out of memory (OOM)

   If vLLM fails with CUDA out of memory: lower --gpu-memory-utilization (default 0.9), reduce --max-model-len, or for large models use --tensor-parallel-size N to shard across multiple GPUs.

Further Reading

• NVIDIA Drivers & CUDA — GPU driver installation
• Ollama — simplest way to get started
• Claude Code — AI coding assistant