Self-Hosted LLM Inference & Serving in 2026: vLLM vs Ollama vs LocalAI vs TGI (+ Open WebUI)

Sending confidential company data, source code, and customer records to commercial LLM APIs exposes organizations to privacy breaches, unexpected rate limits, and runaway monthly bills.

With open-weight models like DeepSeek-R1 / V3, Llama 3.3 70B, Mistral NeMo, and Qwen 2.5, running local inference on your own hardware (or dedicated GPU cloud instances like Hetzner, RunPod, or Lambda) is faster and more cost-effective than ever.

However, selecting the right inference engine determines whether you achieve 5 tokens/sec or 120 tokens/sec per GPU.

In this guide, we break down the four leading self-hosted LLM serving engines and provide production-ready Docker Compose configurations.

1. Engine Comparison Matrix

2. Choosing Your Serving Architecture

When to Choose vLLM

• You have modern NVIDIA GPUs (RTX 3090, 4090, A100, H100) or AMD ROCm hardware.
• You need to serve multiple concurrent users or background agent workflows without throughput collapse.
• You want state-of-the-art memory management via PagedAttention, eliminating VRAM waste from KV caching.

When to Choose Ollama

• You want zero-configuration model pulling (ollama run llama3.3).
• You are running on consumer hardware, Apple Silicon (M1/M2/M3/M4), or hybrid CPU/GPU setups using GGUF quantization.
• You want seamless integration with Open WebUI, AnythingLLM, or Cursor.

When to Choose LocalAI

• You need a unified engine capable of running text generation, text-to-speech (TTS), audio transcription (Whisper), and image generation (Stable Diffusion) inside a single container.

3. Production Deployment Configurations

Stack 1: High-Performance vLLM + Open WebUI (GPU Production Stack)

This stack exposes an OpenAI-compatible API on port 8000 and the premier ChatGPT-like interface (Open WebUI) on port 3000:

services:
  vllm:
    image: vllm/vllm-openai:latest
    container_name: vllm_server
    restart: unless-stopped
    environment:
      - HUGGING_FACE_HUB_TOKEN=${HF_TOKEN:-}
    volumes:
      - ~/.cache/huggingface:/root/.cache/huggingface
    ports:
      - "127.0.0.1:8000:8000"
    ipc: host
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    command: >
      --model Qwen/Qwen2.5-Coder-7B-Instruct
      --gpu-memory-utilization 0.90
      --max-model-len 8192
      --dtype auto
      --api-key ${VLLM_API_KEY:-super-secret-key-123}

  open-webui:
    image: ghcr.io/open-webui/open-webui:main
    container_name: open_webui
    restart: unless-stopped
    ports:
      - "127.0.0.1:3000:8080"
    volumes:
      - open_webui_data:/app/backend/data
    environment:
      - OPENAI_API_BASE_URL=http://vllm:8000/v1
      - OPENAI_API_KEY=${VLLM_API_KEY:-super-secret-key-123}
      - WEBUI_AUTH=true
      - ENABLE_SIGNUP=false # Disable public registration
    depends_on:
      - vllm

volumes:
  open_webui_data:

Stack 2: Ollama + Open WebUI (CPU & GPU Homelab Stack)

For mixed environments and easy GGUF model handling:

services:
  ollama:
    image: ollama/ollama:latest
    container_name: ollama
    restart: unless-stopped
    ports:
      - "127.0.0.1:11434:11434"
    volumes:
      - ollama_models:/root/.ollama
    # Uncomment deploy block if NVIDIA GPU is present:
    # deploy:
    #   resources:
    #     reservations:
    #       devices:
    #         - driver: nvidia
    #           count: all
    #           capabilities: [gpu]

  open-webui:
    image: ghcr.io/open-webui/open-webui:main
    container_name: open_webui
    restart: unless-stopped
    ports:
      - "127.0.0.1:3000:8080"
    volumes:
      - open_webui_data:/app/backend/data
    environment:
      - OLLAMA_BASE_URL=http://ollama:11434
      - WEBUI_AUTH=true
    depends_on:
      - ollama

volumes:
  ollama_models:
  open_webui_data:

4. Hardware Sizing & VRAM Rules of Thumb

5. Production Hardening Checklist

1. Authentication First: Never expose unauthenticated Ollama (11434) or vLLM (8000) ports to the public internet. Anyone finding the IP can drain your compute or inject prompts.
2. Reverse Proxy & SSL: Wrap your Open WebUI behind Traefik or Caddy with Let's Encrypt certificates.
3. Model Weights Cache: Persist ~/.cache/huggingface and ~/.ollama on fast NVMe volumes to prevent re-downloading 20GB+ weights across container restarts.

Conclusion & Architecture Roadmap

Self-hosting your AI stack provides full data governance, zero compliance risk, and fixed compute costs.

Looking for battle-tested Docker Compose templates for local AI, RAG pipelines, vector databases, and reverse proxies? Check out the complete guides on SelfHostStack or grab the Self-Hosted Starter Stack Pack ($29).