System Requirements

NeoMind runs on desktop (macOS / Windows / Linux) or servers. Below are the requirements for each deployment mode.

Package Sizes & Resource Footprint

Download Sizes (GitHub Releases)

Artifact	Size	Notes
Server binary (tar.gz)	~23–26 MB	neomind + neomind-extension-runner bundled; per-platform
Web frontend (tar.gz)	~5.4 MB	Static assets (HTML/JS/CSS), served by the backend
macOS Desktop (.dmg)	~39 MB	Tauri app — bundles backend + frontend + system WebView
Windows Desktop (.msi)	~39 MB
Linux Desktop (.deb)	~43 MB
Linux AppImage	~112 MB	Fully self-contained — includes all system libraries

Total server footprint: ~30 MB on disk (binary + web assets). No Docker layers, no pip/npm runtime — a single statically compiled binary plus static files.

Runtime Resource Usage

The main process embeds the API server, MQTT broker, redb storage, and rule engine in one binary. Extensions run in separate processes.

Component	RAM (typical)	Notes
Main process	50–150 MB	Axum + MQTT broker + redb. Scales with device count and telemetry volume.
Extension runner	10–50 MB per extension	ML models are lazy-loaded (first command → load → stay resident). A YOLOv8n model adds ~50 MB when active.
Telemetry storage	grows with data	redb file at data/telemetry.redb. ~1 KB per data point. Retention is configurable.

No external dependencies

NeoMind does not require PostgreSQL, Mosquitto, Redis, or any other external service. The only optional external dependency is an LLM — either Ollama running locally, or a cloud API key.

Desktop App (Recommended for Getting Started)

Download the installer from GitHub Releases.

OS	Architecture	Format
macOS	Apple Silicon (arm64)	.dmg
Windows	x86_64	.msi / .exe
Linux	x86_64 / arm64	.AppImage / .deb

Official pre-built packages cover only the architectures above. For other platforms (e.g. macOS Intel / Windows ARM), build from source.

Minimum hardware:

• CPU: 2 cores (4 recommended)
• RAM: 4 GB baseline; 8 GB+ recommended when running a local LLM
• Disk: 1 GB install + data storage (see below)

Server Deployment

Supported Operating Systems

• Linux: Ubuntu 20.04+ / Debian 11+ / CentOS 8+ / other mainstream distros (x86_64 / arm64)
• macOS: 12 Monterey+ (development or small-scale deployment)
• Windows: Windows 10 / Server 2019+ (via WSL2 or native)

Hardware Recommendations

Scenario	CPU	RAM	Disk	Notes
Light (rules only / cloud LLM)	2 cores	2 GB	10 GB	No local model
Recommended (local LLM)	4 cores	8 GB	20 GB+ SSD	Runs qwen3.5:4b and similar small models
Multi-device / vision pipeline	8 cores	16 GB	50 GB+ SSD	Multi-stream video + YOLO/OCR extensions

GPU: Not required. Local LLM and vision inference run via Ollama (CPU mode); when a GPU is present, Ollama auto-accelerates.

Network Ports

Port	Protocol	Purpose	Configurable
9375	HTTP	Backend API + Web UI (default neomind-cli serve port)	--port / PORT env var
1883	MQTT	Embedded MQTT broker (device onboarding)	Config file
80 / 443	HTTP(S)	Reverse proxy (optional, nginx)	nginx config

For production, use an nginx reverse proxy and expose only 80/443 externally; keep 9375 / 1883 on the internal network.

Runtime Dependencies

Server deployment requires no manual dependency installation — the install script downloads statically compiled binaries. Optional components:

• Ollama (recommended): local LLM inference. Install at ollama.com. Pull a model the first time you configure an LLM backend, e.g. ollama pull qwen3.5:4b
• Docker (optional): one-line deploy via docker compose up -d
• nginx (optional): production reverse proxy + static frontend hosting

Data Storage

NeoMind uses embedded storage — no external database is needed. The data directory defaults to data/ and contains:

File / Dir	Purpose
telemetry.redb	Time-series telemetry (all device metrics)
sessions.redb	User sessions
devices.redb / dashboards.redb / rules.redb / agents.redb	Per-domain primary data
messages.redb	Notification delivery records
memory/	Agent memory files (Markdown)
skills/	Skill definitions (YAML + Markdown)
extensions/	Extension binaries and config
logs/	Runtime logs

The data directory can be customized via env vars or install script params (see Install & Setup).

Development Environment

Building from source requires:

Tool	Version	Purpose
Rust	1.85+ (toolchain pinned to 1.92.0)	Backend
Node.js	20+	Frontend
Ollama	any	Local LLM (or connect a cloud model)

# Backend
cargo build && cargo test && cargo run -p neomind-cli -- serve

# Desktop / frontend
cd web && npm install && npm run tauri:dev

See Developer Guide for details.

LLM Backend Requirements

NeoMind supports multiple LLM backends in two deployment modes:

• Local: Ollama (recommended, qwen3.5:4b model). The host needs enough RAM to load the model.
• Cloud: OpenAI / Anthropic / Google / xAI / Qwen / DeepSeek / GLM / MiniMax and other OpenAI-compatible endpoints. Requires an API key and outbound network access.

See Configure LLM Backend for setup.

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Last updated: 2026-06-15