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Installation vom Herausgeber. Kein PlugMCP-Endpunkt.
Host a copy with our keys and logs.
Noch kein paketiertes Release in der Registry. Folgen Sie der README des Repositorys.
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Auf PlugMCP ausführen
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
StartenVom Herausgeber
AI Inference Managed by AI — A single Go binary that detects hardware, resolves optimal configs from a YAML knowledge base, deploys inference engines via K3S, and exposes 61 MCP tools for AI Agents to operate everything.
AI-Inference-Managed-by-AI: Go binary for managing AI inference on edge devices
Grab a pre-built binary from the Releases page, or build from source:
git clone https://github.com/Approaching-AI/AIMA.git
cd AIMA
make build
For published product releases, the binary installer can be one line:
curl -fsSL https://raw.githubusercontent.com/Approaching-AI/AIMA/master/install.sh | sh
On Windows PowerShell:
irm https://raw.githubusercontent.com/Approaching-AI/AIMA/master/install.ps1 | iex
Notes:
vX.Y.Z product release instead of GitHub's latest release, because bundle tags such as bundle/stack/2026-02-26 are not product binaries.AIMA_REPO=<owner>/<repo>.AIMA_VERSION=v0.5.0.windows/amd64 and installs to %LOCALAPPDATA%\Programs\AIMA.# 1. Detect your hardware
aima hal detect
# 2. Initialize infrastructure (installs K3S + HAMi + aima-serve daemon)
# Downloads airgap images for offline container startup.
# --tier k3s pulls in K3S + HAMi on top of the docker baseline.
# Requires root for systemd service installation.
sudo aima onboarding init --tier k3s --yes
# 3. Deploy a model (auto-resolves engine + config for your hardware)
aima deploy qwen3.5-35b-a3b
After aima onboarding init --tier k3s, three components are running as systemd services:
The server is now discoverable on the LAN and ready to serve inference requests.
On another device with the AIMA binary — no onboarding init or serve needed:
# List AIMA devices auto-discovered on the LAN via mDNS (no IP needed)
aima fleet devices
# Query a remote device
aima fleet exec <device-id> hardware.detect
aima fleet exec <device-id> deploy.list
# Call the OpenAI-compatible API directly
curl http://<server-ip>:6188/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{"model":"qwen3.5-35b-a3b","messages":[{"role":"user","content":"hello"}]}'
Every AIMA server hosts a built-in Web UI at http://<server-ip>:6188/ui/.
To discover the server IP first: aima fleet devices.
To get a Fleet dashboard that auto-discovers all LAN peers, run aima serve --discover on your own device and open http://localhost:6188/ui/.
aima onboarding init starts the server without authentication (LAN trust model). To enable API key authentication:
# Set API key (hot-reloads, no restart needed)
aima config set api_key <your-key>
# All API/MCP/Fleet requests now require: Authorization: Bearer <your-key>
# Web UI will prompt for the key automatically.
# Remote fleet commands with authentication
aima fleet devices --api-key <your-key>
AIMA has been validated end-to-end across eight GPU/NPU ecosystems — NVIDIA (CUDA), AMD (ROCm / Vulkan), Huawei Ascend (CANN), Hygon (DCU), MetaX (MACA), Moore Threads (MUSA), Apple (Metal), and Intel (CPU-only):
AIMA orchestrates three inference runtimes — vLLM (Safetensors), llama.cpp (GGUF), and SGLang (Safetensors) — across every supported backend:
AIMA resolves every deployment decision by climbing four layers of intelligence. Each layer can override the layer below it, and every layer fails gracefully to the one underneath.
The system is built around four invariants: no code branches for engine/model types (YAML-driven), no container lifecycle management (K3S handles it), MCP tools as the single source of truth, and offline-first operation.
See design/ARCHITECTURE.md for the full architecture document.
Every AIMA release passes through The Forge — an end-to-end UAT matrix run on real hardware.
Per-release UAT report lives in docs/uat/v0.4-release-uat.md, with raw per-device evidence (logs, configs, reproducer commands) under artifacts/uat/v0.4/ — u1…u15 covering install, hardware detect, model deploy, API/MCP, fleet, onboarding, and failover on GB10, RTX 4090, AMD W7900D, Apple M4, and others.
AIMA is built to be driven by AI agents first, humans second.
See internal/mcp/ for the complete registry.
Connect any MCP-compatible client (Claude Desktop, Cursor, custom agent) and AIMA becomes your AI-inference control plane.
cmd/aima/ Entry point + dependency wiring split by domain
internal/
hal/ Hardware detection
knowledge/ YAML knowledge base + SQLite resolver
runtime/ K3S (Pod) + Docker (container) + Native (exec) runtimes
mcp/ MCP server + 61 MCP tool registrations/implementations
agent/ Go Agent loop (L3a)
cli/ Cobra CLI (thin wrappers over MCP tools)
ui/ Embedded Web UI (Alpine.js SPA)
proxy/ OpenAI-compatible HTTP proxy
fleet/ mDNS fleet discovery + remote execution
sqlite.go SQLite state store (`package state`, modernc.org/sqlite, zero CGO)
model/ Model scan/download/import + metadata detection
engine/ Verified container/native engine lifecycle
stack/ K3S + HAMi infrastructure installer
catalog/
hardware/ Hardware profile YAML
engines/ Engine asset YAML
models/ Model asset YAML
partitions/ Partition strategy YAML
stack/ Stack component YAML
make build
# Output: build/aima (or build/aima.exe on Windows)
make all
# Output:
# build/aima.exe (windows/amd64)
# build/aima-darwin-arm64 (macOS/arm64)
# build/aima-linux-arm64 (linux/arm64)
# build/aima-linux-amd64 (linux/amd64)
make release-assets
# Output:
# build/release/<version>/aima-darwin-arm64
# build/release/<version>/aima-linux-amd64
# build/release/<version>/aima-linux-arm64
# build/release/<version>/aima-windows-amd64.exe
# build/release/<version>/checksums.txt
To upload those assets to the matching GitHub release with gh:
make publish-release-assets
Annotated SemVer tag pushes such as v0.5.0 also trigger .github/workflows/release.yml, which builds the same assets and uploads them automatically.
go test ./...
Apache License 2.0. See LICENSE for details.
AI-Inference-Managed-by-AI: Go binary for managing AI inference on edge devices
Tools
Noch keine Tool-Liste. Remote-Server erhalten eine nach dem ersten Health-Check.
FAQ
Kopieren Sie auf dieser Seite die stdio-Konfiguration für Cursor, Claude Desktop, Claude Code oder VS Code. Sie nutzt die Installation des Herausgebers, keinen PlugMCP-Proxy.
In diesem Eintrag ist keine erforderliche Umgebungsvariable aufgeführt.
AIMA läuft lokal über stdio.
Zuletzt geprüft: 2026-10-28. AI-Inference-Managed-by-AI: Go binary for managing AI inference on edge devices