aquila 0.3.5

Single control plane for multi-node vLLM inference — deploy, serve, and manage LLMs across a GPU cluster without Kubernetes.

Aquila

Single control plane for multi-node vLLM inference. Point-and-click deployments, an OpenAI-compatible gateway, warm caching, live GPU monitoring, and a full deployment lifecycle — without Kubernetes or a managed platform.

Quick start

uv venv && source .venv/bin/activate
uv pip install aquila

Host (management server):

aquila host up --host-ip 0.0.0.0 --host-frontend-port 5173 --host-discover-port 11400

Client (each GPU node):

aquila client up --host-ip <host-ip> --host-discover-port 11400

Open http://<host-ip>:5173 — client nodes appear within seconds. Add --service for persistent systemd services.

Features

• Deploy and manage models across GPU nodes via Docker or rootless Podman — each runs in the official vllm/vllm-openai container with a specific version, nightly build, or commit hash.
• OpenAI-compatible gateway (/v1) with stable URLs across node moves, API key auth with per-deployment scoping, and auto-expiring snippet keys.
• Warm cache — pause idle models to RAM and resume on demand; LRU auto-eviction frees GPU VRAM while keeping weights ready for near-instant restart.
• Local checkpoints and LoRA adapters — upload from the browser (streamed) or pull from a URL directly onto a node.
• Live monitoring — GPU utilization, disk usage, deployment status, per-deployment usage metrics, and 48-hour metric history charts.
• Usage tracking — lifetime tokens, request counts, and average prefill/generation speeds from vLLM's own metrics.
• Reproducibility manifests — export model, HF revision, seed, vLLM version, image digest, and full config per deployment.
• Notifications — Slack/webhook alerts when deployments become ready, fail, or are about to expire.
• Per-GPU maintenance — cordon individual GPUs while the rest of the node keeps serving; optionally drain affected deployments.
• Extra packages and plugins — install pip packages and upload vLLM plugins per deployment via cached derived images.
• Reverse proxy support — deploy behind nginx at any sub-path with --base-path.

Best for

• Research labs and university clusters
• Teams sharing GPUs across projects
• Self-hosted multi-model inference

Supported platforms

• Python 3.10–3.14, Node.js ≥ 23 (host only)
• Ubuntu 22.04 and 24.04
• NVIDIA GPUs (H100, A100, L40, RTX 4090, DGX Spark)

Full documentation