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01-ollama.md

kstack: book: Centerpoint Home Lab chapter: AI & Automation page: Ollama tags: [ollama, llm, ai, cuda, gpu, inference]

Overview

emptyOllama is the local large language model (LLM) inference backend for the homelab. It serves models via an OpenAI-compatible REST API and is consumed by Open Web UI, PaperlessAI, and any other service that needs LLM inference without sending data to external providers.

Ollama runs with full NVIDIA RTX 5080 acceleration via the nvidia container runtime. All model weights are stored on the local NVMe system drive.

Access

Type URL Notes Internal https://ollama.home.local Traefik-proxied HTTPS Direct http://192.168.1.85:11434 Raw API (no TLS)

No external (internet-facing) route — LAN and Tailscale access only.

Configuration

Image: ollama/ollama:latest Compose project: ai-stack Runtime: nvidia

Ports

Port Protocol Purpose 11434 TCP Ollama REST API (host-bound)

Traefik Labels

traefik.enable: "true"
traefik.docker.network: traefik-net
traefik.http.routers.ollama.rule: Host(`ollama.home.local`)
traefik.http.routers.ollama.entrypoints: websecure
traefik.http.routers.ollama.tls.certresolver: step-ca
traefik.http.services.ollama.loadbalancer.server.port: 11434

Internal-only route, no authentication middleware — API access is unrestricted on the LAN. Callers must be on the LAN or Tailscale.

Environment Variables

Variable Value Purpose OLLAMA_HOST 0.0.0.0 Listen on all interfaces NVIDIA_VISIBLE_DEVICES all Expose all NVIDIA GPUs to container NVIDIA_DRIVER_CAPABILITIES compute,utility Required NVIDIA driver caps OLLAMA_NUM_GPU 999 Use all available GPU layers no_proxy localhost,127.0.0.1 Bypass proxy for local calls

GPU Acceleration

Ollama uses the nvidia container runtime. The RTX 5080 provides 16 GB of VRAM, allowing large models (7B–27B parameter range) to run fully in VRAM without CPU offloading.

runtime: nvidia
environment:
  - NVIDIA_VISIBLE_DEVICES=all
  - NVIDIA_DRIVER_CAPABILITIES=compute,utility

Volumes / Bind Mounts

Host Path Container Path Purpose /home/jeeves/docker/ollama/ /root/.ollama Model weights and config

Model files are stored under /home/jeeves/docker/ollama/models/ on the local NVMe system drive (1.8 TB). Large models can consume significant space.

Networks

Network Purpose ai-stack_ai-internal Internal communication with Open Web UI, PaperlessAI traefik-net Exposes Ollama API via Traefik

Dependencies

    NVIDIA container runtime (nvidia) on the Docker daemon RTX 5080 connected via OcuLink (must be recognised as a CUDA device)

    Notes / Gotchas

      OLLAMA_NUM_GPU=999 is the conventional way to tell Ollama to use as many GPU layers as possible. It does not literally use 999 GPUs. Models are downloaded via ollama pull <model> or via the Open Web UI admin panel. Downloaded models persist in the bind-mounted /root/.ollama directory. If the OcuLink connection drops or the GPU is not recognised, Ollama falls back to CPU inference — responses will be significantly slower. Check with: 
      docker exec -it ollama ollama ps
      The local file at /home/jeeves/docker/ai-stack/ollama-intel-arc/docker-compose.yml is the legacy IPEX-LLM config and is no longer in use. The current stack is managed via Portainer and uses ollama/ollama:latest with CUDA.

      Last Updated: 2026-06-16

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