GPUStack

Categories: Coding & Developer Tools, Automation / Agents, Business & Sales | Pricing: Freemium | Official Website ↗

GPUStack is an enterprise AI infrastructure platform for deploying, governing, and scaling LLMs and GPU compute on any hardware.

GPUStack is an open-source platform designed to manage and scale AI models and GPU compute resources across on-premise, cloud, or hybrid environments. It provides a unified workflow to abstract the complexities of the AI inference stack, enabling users to connect model sources, auto-select inference engines, scale with distributed inference, and serve models via standard APIs. The platform supports a wide range of GPUs from various vendors and integrates with popular inference engines like vLLM, SGLang, and TensorRT-LLM. GPUStack offers two main services: Token as a Service (TaaS) for full lifecycle management of AI models, including deployment, traffic routing, performance tuning, and observability; and GPU as a Service (GPUaaS) for provisioning and managing GPU instances with persistent storage and flexible access. It includes enterprise-ready features such as RBAC, multi-tenancy, SSO integration, API key management, IP allowlisting, token quotas, usage analytics, and high availability. The platform also provides a unified web UI for managing models, GPU clusters, users, and API keys.

Key Features

Token as a Service (TaaS)
GPU as a Service (GPUaaS)
Support for heterogeneous GPUs (NVIDIA, AMD, Ascend, etc.)
Automated inference engine selection (vLLM, SGLang, TensorRT-LLM)
Distributed inference scaling (Tensor parallel, pipeline parallel)
OpenAI-compatible and Anthropic-compatible APIs
Role-Based Access Control (RBAC) & Multi-Tenancy
SSO Integration (OIDC, SAML, AD/LDAP)

Pros

Supports a wide range of GPU hardware from multiple vendors.
Simplifies complex AI inference stack management with a unified workflow.
Offers significant performance gains over unoptimized baselines.
Provides enterprise-grade security and compliance features.
Fully open-source core with an Apache 2.0 license.

Cons

Pricing details for the Enterprise Edition are not publicly listed.
Requires technical expertise for installation and configuration.
Focuses primarily on inference, not training workflows.
No explicit mention of a free trial for the Enterprise Edition.
Limited information on specific integrations beyond general categories.

Use Cases

Deploying and managing large language models (LLMs) in production
Orchestrating GPU resources across on-premise and cloud environments
Building 'Token Factories' for AI inference at scale
Optimizing AI model performance and throughput
Ensuring secure and compliant AI deployments

Best For

Enterprises deploying and scaling AI models
Organizations with heterogeneous GPU infrastructure
Developers requiring a unified AI inference platform
Teams needing robust security and governance for AI workloads

Integrations: Hugging Face, ModelScope, vLLM, SGLang, llama.cpp, TensorRT-LLM, MindIE, OpenAI (compatible API), Anthropic (compatible API), OpenWebUI

Platforms: Web

Watch demo on YouTube ↗

View full GPUStack profile on Tools-Radar | Browse Coding & Developer Tools tools | Alternatives to GPUStack

Tools-Radar is a free directory of 10,000+ AI tools — discover, compare, and choose the right AI software for your needs. Visit tools-radar.com