GPUStack
Open-source GPU cluster manager for high-performance AI model serving.
About
GPUStack is an open-source GPU cluster manager designed for efficient AI model deployment. It manages GPU clusters across on-premises servers, Kubernetes, and cloud providers, and automatically configures pluggable high-performance inference engines — vLLM, SGLang, TensorRT-LLM, or your own — with pre-tuned modes for low latency or high throughput.
It supports heterogeneous hardware (GPUs in MacBooks, Windows PCs, and Linux servers), distributed single-node and multi-node inference, OpenAI-compatible APIs, speculative decoding (EAGLE3, MTP), extended KV-cache systems like LMCache, plus user/API-key management, GPU monitoring, and on-demand SSH-accessible GPU instances. The honest caveat: it is a younger project in a crowded inference-serving space (competing with raw vLLM/SGLang deployments and commercial platforms), so teams should validate its maturity against their reliability bar — but as an open cluster manager with day-0 model support, it fills a real gap.
What it can do
- Multi-cluster GPU management across on-prem, K8s, and cloud
- Pluggable inference engines (vLLM, SGLang, TensorRT-LLM)
- OpenAI-compatible APIs with auth and rate limits
- Distributed inference and speculative decoding
- GPU monitoring plus on-demand SSH GPU instances
Setup
Install the GPUStack server and workers via installers (Windows/macOS/Debian) or the install script; add GPUs, deploy models from the UI.
Prerequisites
Linux/macOS/Windows machines with GPUs (NVIDIA/AMD/Intel).
Use cases
Works with
Trust score — 8/10
Scored 8/10 (verified tier, metrics-v1). Evidence: Verified via vendor site/docs/GitHub and reputable coverage, wave 77; Trending score 76/100 from researcher assessment.
Sources
Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.