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ODS V3

Osmantic Deployment System

Osmantic

Turn your PC, Mac, or Linux box into a private AI server.

AI server and homelab setup is rapidly becoming a solved problem. It should feel that way for everyone.

License: Apache 2.0 + Pixel ODS-only GitHub Stars ODS V3 Release

Watch the demo

ODS installs and wires together everything you need to run AI locally, so you do not have to assemble Ollama, Open WebUI, n8n, ComfyUI, and privacy tools by hand:

  • Local model inference — run open models on your own hardware
  • ChatGPT-style web UI — talk to your models from any browser
  • Control dashboard — manage models, services, setup, GPU status, and extensions from one place
  • Voice, agents, and workflows — build automations that can listen, speak, call tools, and get work done
  • RAG and search — connect local documents, private search, and retrieval workflows
  • Image generation — run local image tools without sending prompts to a hosted API
  • Privacy and ops — keep service auth, secrets, observability, and diagnostics in one local stack

No cloud required. No subscriptions required. Your prompts and data stay on your machine unless you choose otherwise. Cloud and hybrid API modes are optional when you want them.

Release validation: Operational changes are checked with a release-grade fleet and distro lab: zero-prereq bootstrap, fresh installs, product flows, full-model capabilities, lifecycle recovery, and the final User Green gate. See Release Validation for what a green run proves.

ODS V3 published: ODS V3 (v3.0.0) is available as a pinned source release. Full fleet qualification is incomplete; see the V3 notes and the promotion record for known task limitations, available evidence, and remaining release gates.

Repo layout: the repository root holds the public README, installers, security policy, GitHub workflows, and project coordination docs. The ods/ directory is the product runtime: services, installer phases, compose overlays, dashboard, CLI, tests, and operator docs.

Release consumption: v3.0.0 is the latest published source release. Its GitHub Latest designation does not establish full fleet qualification. main continues receiving fixes; pin a tag or audited commit and retain its validation receipt when reproducibility matters. V3 fixes land on main; release/2.6.x is the older 2.6 maintenance lane. See Release Channels, Installer Trust, and Forkability.

September main update: the quickstarts below follow development main, including the September Portal/platform promotion. That merge is not a new stable release or proof of complete fleet qualification. Native Pixel source-update and backup/recovery limits are documented in Source Updates. Use a pinned release or audited commit when reproducibility is required.

Get Started

Choose your system, copy the block, run it in a normal terminal. ODS installs the stack, picks a model for your hardware, starts the services, and gives you the local web UI.

Linux or macOS

curl -fsSL https://install.osmantic.com/ods.sh | bash

Windows PowerShell

$ProgressPreference = "SilentlyContinue"
$odsSrc = Join-Path $env:TEMP ("ods-install-" + [guid]::NewGuid().ToString("N"))
$odsZip = Join-Path $odsSrc "ods-main.zip"
New-Item -ItemType Directory -Path $odsSrc | Out-Null
Invoke-WebRequest "https://github.com/Osmantic/ODS/archive/refs/heads/main.zip" -OutFile $odsZip
Expand-Archive -LiteralPath $odsZip -DestinationPath $odsSrc -Force
cd (Get-ChildItem -LiteralPath $odsSrc -Directory | Select-Object -First 1).FullName
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass
.\install.ps1

Prerequisites: Docker must be installed and running. On Windows, use Docker Desktop with the WSL2 backend enabled and run the block in a normal, non-Administrator PowerShell window.

The hosted Linux/macOS endpoint proxies the current bootstrap from repository main. Reviewed merges reach it automatically after edge-cache refresh. ODS_REF selects a compatible repository checkout. See Installer Trust to inspect the script or install a stable release or audited commit manually.

Windows users should not run the curl ... | bash command from PowerShell. The PowerShell block above downloads the source ZIP and runs the same Windows installer used by the clone-based workflow. For more detail, see the Windows Quickstart.

After install, open http://localhost:3000 and start chatting.

Uninstall later with the matching platform command:

cd ~/ods
./ods-uninstall.sh --force
$installDir = "$env:USERPROFILE\ods"
cd $installDir
.\ods.ps1 uninstall --force

Windows recovery note: if the runtime folder is partial and .\ods.ps1 is missing, run the same command from a source checkout as .\ods\installers\windows\ods.ps1 uninstall --force. It removes Docker resources labelled as the ODS compose project before removing the runtime directory.

API endpoint: Linux Docker installs expose llama-server on http://localhost:11434 by default (OLLAMA_PORT) while containers use llama-server:8080. macOS native Metal and Windows native/Lemonade paths use http://localhost:8080 unless overridden. Open WebUI stays on http://localhost:3000.

No GPU? ODS also runs in cloud mode — same full stack, powered by OpenAI/Anthropic/Together APIs instead of local inference:

./install.sh --cloud

Port conflicts? Every port is configurable via environment variables. See .env.example for the full list, or override at install time:

WEBUI_PORT=9090 ./install.sh

New here? Read the Friendly Guide or listen to the audio version — a complete walkthrough of what ODS is, how it works, and how to make it your own. No technical background needed.


At A Glance

Question Answer
What is it? A local AI server stack for your own hardware, with a one-command Linux/macOS installer and a PowerShell installer for Windows.
Who is it for? People who want private AI at home, in a lab, or on a workstation without hand-wiring a dozen services.
What do I get? Local inference, Open WebUI chat, a control dashboard, voice, agents, workflows, RAG, search, image generation, privacy tools, observability, and developer tools.
What does it run on? Linux, Windows with WSL2/Docker Desktop, and macOS Apple Silicon.
Is cloud required? No. Local mode is the default; cloud and hybrid API modes are optional.
If you know... ODS adds...
Ollama / llama.cpp The surrounding server stack: chat, dashboard, voice, RAG, workflows, agents, privacy, and service management.
Open WebUI A full installer and control plane around Open WebUI, plus pre-wired local services.
AnythingLLM Broader local AI appliance behavior beyond RAG: inference, chat, voice, workflows, image generation, and ops.
n8n self-hosted AI starter kits Workflow automation as one part of a larger private AI server.

Current Platform Support

Platform Status
Linux (NVIDIA + AMD Strix Halo) Supported — see the hardware and distro limits in the support matrix
Linux + Intel Arc (SYCL) Experimental / Tier C — validation is hardware-specific
Windows (NVIDIA + AMD) Supported — install and run today
macOS (Apple Silicon) Supported — install and run today

Tested Linux distros: Ubuntu 26.04/24.04/22.04, Debian 12, Linux Mint 21.3, Fedora 41+, Rocky Linux 9, Arch Linux, Manjaro, CachyOS, and openSUSE Tumbleweed. Other distros using apt, dnf, pacman, or zypper should also work — open an issue if yours doesn't.

Release validation: Operational changes run through a release-grade gate that covers zero-prereq bootstrap, clean installs, product behavior, full-model capabilities, lifecycle recovery, and User Green. See Release Validation and the Validation Matrix.

Windows: Requires Docker Desktop with WSL2 backend. NVIDIA GPUs use Docker GPU passthrough; AMD Strix Halo runs through the platform-specific accelerated path documented in the Windows installer and support matrix.

macOS: Requires Apple Silicon (M1+) and Docker Desktop. llama-server uses native Metal acceleration; Portal's gateway and managed host helpers also run natively. The UI, ingress, sandbox and supporting services run in Docker. See the macOS Quickstart.

See the Support Matrix for supported platform claims and the Validation Matrix for the layered test surface used to test those claims.


Why ODS?

A handful of companies control the vast majority of global AI traffic — and with it, your data, your costs, and your uptime. Every query you send to a centralized provider is business intelligence you don’t own, running on infrastructure you don’t control, priced on terms you can’t negotiate.

If AI is becoming critical infrastructure, it shouldn’t be rented. Self-hosting local AI should be a sovereign human right, not a career choice.

Because running your own AI shouldn't require a CS degree and a weekend of debugging CUDA drivers. Right now, setting up local AI means stitching together a dozen projects, writing Docker configs from scratch, and praying everything talks to each other. Most people give up and go back to paying OpenAI.

We built ODS so you don't have to.

  • One command — detects your GPU, picks the right model, generates credentials, launches everything
  • Chatting in under 2 minutes — bootstrap mode gives you a working model instantly while your full model downloads in the background
  • Full service stack, pre-wired — chat, agents, voice, workflows, search, RAG, image generation, privacy tools, observability, and developer tools. All talking to each other out of the box
  • Fully moddable — every service is an extension. Drop in a folder, run ods enable, done
Manual install (Linux)
git clone https://github.com/Osmantic/ODS.git
cd ODS/ods
./install.sh
Windows (PowerShell)

Requires Docker Desktop with WSL2 backend enabled. Install Docker Desktop first and make sure it is running before you start.

Open a normal PowerShell session and run:

$ProgressPreference = "SilentlyContinue"
$odsSrc = Join-Path $env:TEMP ("ods-install-" + [guid]::NewGuid().ToString("N"))
$odsZip = Join-Path $odsSrc "ods-main.zip"
New-Item -ItemType Directory -Path $odsSrc | Out-Null
Invoke-WebRequest "https://github.com/Osmantic/ODS/archive/refs/heads/main.zip" -OutFile $odsZip
Expand-Archive -LiteralPath $odsZip -DestinationPath $odsSrc -Force
cd (Get-ChildItem -LiteralPath $odsSrc -Directory | Select-Object -First 1).FullName
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass
.\install.ps1

The Set-ExecutionPolicy command allows the installer script to run in the current session. It does not change your system-wide policy. Running as Administrator is not recommended for the installer because user-level paths such as .opencode, data/, and .env can be created with admin-owned permissions.

The installer detects your GPU, picks the right model, generates credentials, starts all services, and creates a Desktop shortcut to the Dashboard. Manage from the runtime directory with .\ods.ps1 status; uninstall with .\ods.ps1 uninstall --force.

macOS (Apple Silicon)

Requires Apple Silicon (M1+) and Docker Desktop. Install Docker Desktop first and make sure it is running before you start.

git clone https://github.com/Osmantic/ODS.git
cd ODS/ods
./install.sh

The installer detects your chip, picks the right model for your unified memory, launches llama-server natively with Metal acceleration, and starts all other services in Docker. Manage with ./ods-macos.sh status.

See the macOS Quickstart for details.


What's In The Box

Chat & Inference

  • Open WebUI — full-featured chat interface with conversation history, web search, document upload, and 30+ languages
  • llama-server — high-performance LLM inference with continuous batching, auto-selected for your GPU; Linux Docker host API defaults to localhost:11434, native macOS/Windows paths use localhost:8080, and container API runs on 8080
  • LiteLLM — API gateway supporting local/cloud/hybrid modes
  • TEI Embeddings — text embedding service for RAG and search workflows

Voice

  • Whisper — speech-to-text
  • Kokoro — text-to-speech

Agents & Automation

  • Portal — bundled core conversational assistant on Apple Silicon macOS and qualified Ubuntu 24.04/26.04 or Debian 12 systemd hosts, including qualified WSL2 installations through the Linux installer. No private repository access or separate license flag is required; available in the Dashboard and through a compatible Open WebUI model route. The native PowerShell installer does not install the Portal host runtime.
  • Hermes Agent — independent general-purpose agent, available alongside Portal; includes memory, skills, and a proxy with optional owner-card gating; direct access by default
  • OpenClaw — deprecated legacy autonomous agent, still opt-in during the migration window
  • n8n — workflow automation with 400+ integrations (Slack, email, databases, APIs)
  • APE — Agent Policy Engine for auditing and governing autonomous tool calls
  • OpenCode — browser-based AI coding assistant wired to the local stack
  • Memory Shepherd — host/systemd helper for agent memory lifecycle management

Knowledge & Search

  • Qdrant — vector database for retrieval-augmented generation (RAG)
  • SearXNG — self-hosted web search (no tracking)
  • Perplexica — deep research engine
  • Brave Search — optional paid Brave Search API integration

Creative

  • ComfyUI — node-based image generation

Privacy & Ops

  • Privacy Shield — PII scrubbing proxy for API calls
  • Dashboard — real-time GPU metrics, service health, model management
  • Dashboard API — service health, setup, status, metrics, and management API behind the dashboard
  • Token Spy — token usage monitor for local and proxied LLM traffic
  • Langfuse — optional LLM observability and tracing

Hardware Auto-Detection

The installer detects your GPU and first assigns a deterministic hardware tier. Linux and macOS then run the versioned catalog selector (ods/scripts/select-model.py), while Windows uses the PowerShell catalog selector in ods/installers/windows/lib/tier-map.ps1; both read ods/config/model-library.json to choose the best installable GGUF for the detected memory envelope. The final choice is written to .env as LLM_MODEL, GGUF_FILE, MAX_CONTEXT, and MODEL_RECOMMENDATION_*.

MODEL_PROFILE=qwen is the default non-Gemma catalog profile, so the effective pick can be Qwen, Phi, or DeepSeek depending on what fits best. MODEL_PROFILE=gemma4 forces Gemma 4 where available, and MODEL_PROFILE=auto uses Gemma 4 on NVIDIA, Apple Silicon, and Intel Arc tiers. Override tier selection with ./install.sh --tier 3; override the model family with MODEL_PROFILE=gemma4 ./install.sh or MODEL_PROFILE=auto ./install.sh.

When the Hermes fallback is enabled, installers keep the first-run bootstrap model at a 64K context floor and promote the full local model context to 128K where the selected model supports it. That avoids Hermes's hard 64K minimum while preserving the under-2-minute first chat experience. The examples below are current catalog-selector outputs for common hardware envelopes; exact installs can differ with detected VRAM/RAM, host architecture, existing downloads, or explicit profile overrides. Throughput still needs a local benchmark after first launch.

NVIDIA

Tier / envelope Current default catalog pick Context Example hardware
0 / 8 GB CPU fallback Qwen3.5 2B (Q4_K_M) 8K Low-RAM CPU-only
1 / 8 GB discrete VRAM Qwen3.5 9B (Q4_K_M) 32K RTX 4060, RTX 3060 12GB
2 / 12 GB discrete VRAM Phi-4 14B (Q4_K_M) 16K RTX 4070-class cards
3 / 24 GB discrete VRAM Qwen3.5 27B (Q4_K_M) 32K RTX 4090, A6000
4 / 48 GB discrete VRAM DeepSeek R1 Distill Llama 70B (Q4_K_M) 32K A6000 Ada, L40S
NV_ULTRA / 90+ GB amd64 discrete VRAM Qwen3 Coder Next (Q4_K_M) 128K Multi-GPU A100/H100
NV_ULTRA / 90+ GB arm64 unified memory Qwen3.6 35B-A3B (UD-Q4_K_M) 128K DGX Spark / GB10-class hosts

AMD Strix Halo (Unified Memory)

Tier / envelope Current default catalog pick Context Hardware
SH_COMPACT / 64 GB unified RAM Qwen3.6 35B-A3B (UD-Q4_K_M) 128K Ryzen AI MAX+ 395 (64GB)
SH_LARGE / 96 GB unified RAM DeepSeek R1 Distill Llama 70B (Q4_K_M) 32K Ryzen AI MAX+ 395 (96GB)
SH_LARGE / 124 GB unified RAM Qwen3.6 35B-A3B (UD-Q4_K_M) 128K Ryzen AI MAX+ 395 (128GB class)

The selector routes unified-memory hosts away from Qwen3 Coder Next when that model would otherwise be selected, because current repo policy documents correctness issues on those backends.

Apple Silicon (Unified Memory, Metal)

Tier / envelope Current default catalog pick Context Example hardware
0 / 8 GB unified RAM Phi-4 Mini (Q4_K_M) 128K M1/M2 base (8GB)
1 / 16 GB unified RAM Qwen3.5 9B (Q4_K_M) 32K M4 Mac Mini (16GB)
2 / 32 GB unified RAM Phi-4 14B (Q4_K_M) 16K M4 Pro Mac Mini, M3 Max MacBook Pro
3 / 48 GB unified RAM Qwen3.5 27B (Q4_K_M) 32K M4 Pro (48GB), M2 Max (48GB)
4 / 64+ GB unified RAM Qwen3.6 35B-A3B (UD-Q4_K_M) 128K M2 Ultra Mac Studio, M4 Max (64GB+)

Intel Arc (Linux, SYCL)

Tier / envelope Current default catalog pick Context Example hardware
ARC_LITE / 6 GB discrete VRAM Phi-4 Mini (Q4_K_M) 128K Arc A380
ARC_LITE / 8 GB discrete VRAM Qwen3.5 9B (Q4_K_M) 32K Arc A750
ARC / 16 GB discrete VRAM Phi-4 14B (Q4_K_M) 16K Arc A770 16GB, newer Arc GPUs

Gemma 4 profile tiers remain in the installer tier maps: E2B on entry hardware, E4B on midrange hardware, 26B-A4B on pro hardware, and 31B on large/ultra hardware.


Bootstrap Mode

No waiting for large downloads. ODS uses bootstrap mode by default:

  1. Downloads a tiny 1.5B model in under a minute
  2. You start chatting immediately
  3. The full model downloads in the background
  4. Hot-swap to the full model when it's ready — zero downtime

The bootstrap model starts with a 64K context window so Hermes can work during the first session. After the background download finishes, ODS swaps to the full model and restores the Hermes/full-model context target.

Skip bootstrap: ./install.sh --no-bootstrap


Switching Models

The installer picks a model for your hardware, but you can switch anytime:

ods model current              # What's running now?
ods model list                 # Show all available tiers
ods model swap T3              # Switch to a different tier

If the new model isn't downloaded yet, pre-fetch it first:

./scripts/pre-download.sh --tier 3    # Download before switching
ods model swap T3                    # Then swap (restarts llama-server)

Already have a GGUF you want to use? Drop the single .gguf file in data/models/, then open Dashboard -> Models and load the local entry. For older installs or headless maintenance, update GGUF_FILE and LLM_MODEL in .env, then restart with the CLI:

ods restart llm

Or restart the container directly from the installed ods directory:

docker compose restart llama-server

Rollback is automatic — if a new model fails to load, ODS reverts to your previous model.


Extensibility

ODS is designed to be modded. Every service is an extension — a folder with a manifest.yaml and a compose.yaml. The dashboard, CLI, health checks, and compose stack all discover extensions automatically.

extensions/services/
  my-service/
    manifest.yaml      # Metadata: name, port, health endpoint, GPU backends
    compose.yaml       # Docker Compose fragment (auto-merged into the stack)
ods enable my-service     # Enable it
ods disable my-service    # Disable it
ods list                  # See everything

The installer itself is modular — 19 library modules, a shared service registry, and 13 ordered phases. Want to add a hardware tier, swap a default model, or skip a phase? Start with the installer architecture map so you update the Linux, macOS, Windows, upgrade, and host-agent writers together.

Full extension guide | Installer architecture


ods-cli

The ods CLI manages your entire stack:

ods status                # Health checks + GPU status
ods list                  # All services and their state
ods logs llm              # Tail logs (aliases: llm, stt, tts)
ods restart [service]     # Restart one or all services
ods start / stop          # Start or stop the stack

ods mode cloud            # Switch to cloud APIs via LiteLLM
ods mode local            # Switch back to local inference
ods mode hybrid           # Local primary, cloud fallback

ods model swap T3         # Switch to a different hardware tier
ods enable n8n            # Enable an extension
ods disable whisper       # Disable one

ods config show           # View .env (secrets masked)
ods preset save gaming    # Snapshot current config
ods preset load gaming    # Restore it

How It Compares

Other tools get you part of the way. ODS gets you the whole way.

ODS Ollama + Open WebUI LocalAI
Scope Full AI stack — inference to agents to workflows LLM + chat LLM only
One-command install Everything, auto-configured LLM + chat only LLM only
Hardware auto-detect + model selection NVIDIA + AMD Strix Halo + Apple Silicon + Intel Arc + CPU/cloud fallback No No
AMD APU unified memory support Platform-specific accelerated backend, selected by installer Partial (Vulkan) No
Autonomous AI agents Bundled Portal on qualified hosts; Hermes available alongside it; OpenClaw legacy opt-in No No
Workflow automation n8n (400+ integrations) No No
Voice (STT + TTS) Whisper + Kokoro No No
Image generation ComfyUI No No
RAG pipeline Qdrant + embeddings No No
Extension system Manifest-based, hot-pluggable No No
Multi-GPU Yes (NVIDIA) Partial Partial

Documentation

Quickstart Step-by-step install guide with troubleshooting
Docs Index Maintained map for operators, contributors, and reviewers
Portal runtime Eligibility, licensing boundary, architecture, install, security, tools, rollback, and qualification
Licensing Apache-2.0 ODS code, Pixel's ODS-only grant, and third-party notices
Build On ODS Forking, custom editions, extension templates, and downstream validation
Forkability How to fork, audit, customize, and independently operate ODS
Maintainer Runbook Release, rollback, validation, and operator continuity guidance for maintainers and forks
High-Risk Change Map Which changes require focused checks, fleet validation, or release-grade gates
Headless Setup QR onboarding, first-boot setup, AP mode, mDNS, and local agent access
Support Matrix Current platform and GPU support status
Release Validation User Green gates and the release-grade fleet/distro validation policy
V3 Release Notes Published V3 source identity and qualification boundaries
2.6.0 Release Notes Historical 2.6 release notes, validation receipt, and known validation boundaries
Validation Matrix Sanitized CI, distro lab, and real-hardware fleet release-readiness evidence
Validation Reproducibility How forks and operators can reproduce the validation story on their own hardware
Offline And Mirroring Pinning, mirroring, and preserving release artifacts for independent operation
Installer Trust Inspect-first install paths, ref pinning, and current provenance limits
Model Management Curated and Hugging Face GGUF discovery, verified imports, switching, and recovery
Hardware Guide What to buy, tier recommendations
FAQ Common questions and configuration
Extensions How to add custom services
Installer Architecture Modular installer deep dive
Installer Phase Contracts Phase ownership, idempotency, failure modes, and validation expectations
Compose Resolver Contracts Rules for compose layers, extensions, backends, ports, and mode overlays
Changelog Version history and release notes
Contributing How to contribute

Contributors And Recognition

ODS is built by a growing group of contributors across installers, GPU support, dashboard, security, extensions, docs, and release validation. The README keeps the product overview focused; the long-form credits, upstream acknowledgements, and contributor history live in CONTRIBUTORS.md.

ODS has been recognized by the local AI and developer community, including AMD Featured Developer recognition, selection as a May 2026 AMD Lemonade Developer Challenge winner, and a feature at (Co)nnect: Philly's AI Ecosystem Summit at Pennovation Works.


License

ODS code is Apache-2.0 except the bundled Pixel source, which has a separate ODS-only use and distribution grant. See Licensing, LICENSE, and Pixel's license.


Built by Osmantic and the growing resistance that refuses to rent what should be owned.

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ODS V3: Turn your PC, Mac, or Linux box into an AI server. LLM inference, chat UI, voice, agents, workflows, RAG, and image generation.

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