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---
created: 2026-10-12 14:07
modified: 2026-10-12 14:07
modified: 2026-10-12 18:05
type: note
tags: [projects/local-llm, infra, ollama, qwen2.5]
tags: [projects/local-llm, infra, ollama, qwen2.5, omniroute]
aliases: [Local LLM Infrastructure, Qwen2.5 Deployment]
---
# [[Local LLM Infrastructure & Qwen2.5 Deployment]]
## 📌 Executive Summary
This project establishes an **on-demand local LLM inference node** on workstation **`sam-4screen-desktop` (`192.168.20.27`)**. It leverages the machine's 64 GB DDR4 RAM and 4-core Intel i7 CPU to run 14-billion parameter models locally (**Qwen2.5-Coder-14B** and **Qwen2.5-14B-Instruct**) without consuming idle background resources or requiring dedicated server GPUs.
This project runs an **on-demand local AI model server** on workstation **`sam-4screen-desktop` (`192.168.20.27`)**.
It uses the machine's 64 GB of RAM and its CPU to run 14-billion parameter Qwen2.5 models, so AI work is possible without cloud cost or cloud dependency.
Other machines and services reach it over the home network.
---
## 🖥️ System Architecture & Machine Roles
## 🖥️ System Architecture
* **Workstation `.27` (`sam-4screen-desktop`):**
* **Role:** Primary Local LLM Host.
* **Hardware:** Intel i7-7700K CPU + **64 GB DDR4 RAM** (~32 GB available for models).
* **Inference Strategy:** **Native CPU execution** using AVX2 instructions (bypasses Docker virtualization overhead for maximum CPU performance).
* **Auto-Unload:** Idle models automatically unload from RAM after 5 minutes (`OLLAMA_KEEP_ALIVE=5m`).
* **Gateway Node `.13` (`nixos-desktop`):**
* **Role:** Network Gateway & OmniRoute Router.
* **Rule:** **Strictly no LLM inference runs on `.13`** due to memory constraints.
```mermaid
flowchart LR
subgraph Clients["Clients"]
NV["Neovim / Avante / CodeCompanion (.27, .13, .51)"]
DASH["Family Console Dashboard (start / stop)"]
end
subgraph Host27["sam-4screen-desktop (192.168.20.27)"]
SYS["NixOS system service: ollama.service"]
ENG["Ollama Engine :11434 - CPU + AVX2"]
MEM["64 GB DDR4 System RAM"]
end
subgraph Router13["nixos-desktop (192.168.20.13)"]
OR["OmniRoute Gateway :20128"]
CLOUD["Cloud fallback: DeepSeek, Claude"]
end
DASH -->|start / stop| SYS
SYS --> ENG
ENG --> MEM
NV -->|direct :11434| ENG
NV -.->|ollama-local/qwen2.5-coder:14b| OR
OR -->|primary route| ENG
OR -.->|when .27 is off| CLOUD
```
---
## 🛠️ On-Demand Management Commands
## 🖧 Machine Roles
Simple terminal commands manage the Ollama service on `.27`:
| Command | Action | Output / Behavior |
| :--- | :--- | :--- |
| `ai-on` | Starts local Ollama service | Binds to `0.0.0.0:11434` for local & LAN access. |
| `ai-off` | Stops local Ollama service | Immediately frees RAM and CPU cycles. |
| `ai-status` | Displays service status | Reports systemd unit state and port listeners. |
* **`.27` (`sam-4screen-desktop`) — AI host.**
Intel Core i7-7700K (4 cores, AVX2) and 64 GB DDR4 RAM, with about 32 GB free for models.
The NVIDIA Quadro P620 drives the displays only; it is not used for inference.
* **`.13` (`nixos-desktop`) — network gateway.**
Runs OmniRoute, Caddy, and DNS. By design it runs **no** model inference, because it has little spare memory.
---
## 🤖 Installed Local Models
## ⚙️ How the Service Works
| Model | Size | Parameter Quantization | Primary Purpose |
The model server is declared in NixOS, which makes it reproducible and easy to audit.
It is **off at boot** so it only uses resources when wanted.
* **Start / stop:** from the family console dashboard.
* **Manual control:** `sudo systemctl start ollama` and `sudo systemctl stop ollama`.
* **Listen address:** `0.0.0.0:11434`, so `.13`, `.51`, and other LAN clients can connect.
* **Warm models:** `OLLAMA_KEEP_ALIVE = -1` keeps the model in RAM while the service runs, so responses are immediate. Stopping the service releases all of that RAM.
---
## 🤖 Installed Models
| Model | Size | Quantization | Primary Purpose |
| :--- | :--- | :--- | :--- |
| `qwen2.5-coder:14b` | 9.0 GB | Q4_K_M GGUF | Offline code generation, refactoring, and code review. |
| `qwen2.5:14b` | 9.0 GB | Q4_K_M GGUF | General drafting, reasoning, and context analysis. |
| `qwen2.5-coder:14b` | 9.0 GB | Q4_K_M GGUF | Code generation, review, and refactoring |
| `qwen2.5:14b` | 9.0 GB | Q4_K_M GGUF | Drafting, reasoning, and general analysis |
Models live in `/var/lib/ollama/models`.
---
## 🌐 Routing Through OmniRoute (verified working)
OmniRoute on `.13` exposes the local models alongside cloud models, with automatic fallback.
| Entry | Value |
| :--- | :--- |
| Base URL | `http://192.168.20.13:20128/v1` |
| Model name | `ollama-local/qwen2.5-coder:14b` |
| Direct to `.27` | `http://192.168.20.27:11434` with model `qwen2.5-coder:14b` |
Verified: chat requests through OmniRoute return HTTP 200 for both models, and the connection reports `test_status: active`.
---
## 💼 Skills & Technical Concepts Demonstrated
* **Linux System Administration:** Systemd user unit management, environment variables, network binding (`0.0.0.0`), process lifecycle control.
* **Local AI & GGUF Inference:** CPU-optimized LLM deployment, RAM budgeting, model quantization trade-offs.
* **Edge & Hybrid Architecture:** On-demand local processing paired with cloud API fallback mechanisms.
* **Linux system administration** — systemd service design, boot policy, sandboxing trade-offs (`DynamicUser`, `ProtectHome`), firewall rules, network binding.
* **Fault diagnosis** — traced a failed system rebuild to two independent causes: stale config symlinks and two services competing for one port.
* **Local AI operations** — CPU inference, GGUF quantization, RAM budgeting, model warm-up and idle policy.
* **Hybrid cloud architecture** — local-first inference with automatic cloud fallback through an API gateway.
---
## 🔗 Related
* Interactive architecture map: [Archify map](https://maps.lab.audasmedia.com.au/ollama_local_llms/docs/)
* AI context (Outline): `docs/outline/00-index.md`