GPUStack

GPUStack is a GPU cluster manager for high-performance AI model serving and on-demand GPU instances.

GPUStack is an enterprise AI infrastructure platform that deploys, manages, and scales large language models and GPU compute resources on any hardware. It provides model as a service and GPU as a service under a single control plane, supporting heterogeneous accelerators including NVIDIA, AMD, and others. The platform connects model sources from Hugging Face and ModelScope, automatically selects inference engines, and scales with distributed inference across multiple nodes and GPUs. It exposes models through OpenAI-compatible and Anthropic-compatible endpoints, serving developers and enterprises with built-in security, role-based access control, and usage analytics. GPUStack differentiates itself by combining cluster orchestration, inference engine mapping, and fine-grained resource allocation into one unified web interface.

GPUStack at a glance

Best for: Software engineer
Runs on: Web, Linux, API, self-hosted, open source
Pricing: Free
Released: May 2024
Category: AI coding tool
Website: gpustack.ai

Key features

Heterogeneous GPU management across multiple accelerator vendors
Automated inference engine selection and hardware mapping
Distributed inference scaling across multiple nodes and GPUs
OpenAI-compatible and Anthropic-compatible standard API endpoints
Role-based access control and multi-tenancy resource isolation
SSO integration with OIDC, SAML, and AD/LDAP protocols
Real-time metrics and resource topology visualization
Token usage analytics, quotas, and metering

Use it for

Deploying and scaling large language models on on-premise or cloud hardware
Provisioning GPU instances with persistent storage and resource allocation
Managing enterprise AI security, API keys, and access permissions
Tracking token usage, request counts, and GPU time for cost visibility

Alternatives to GPUStack

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iwant — Spins up cloud GPUs and open models instantly via a single command.
LMDeploy — Accelerates inference with high-performance engines and quantization tools.
AirLLM — Runs massive 70B+ parameter models on small 4GB consumer GPUs.
Narwhal — It automatically hot-swaps prefill and decode roles without reloading model weights.
llm-d — Deploys production-grade LLM serving on existing hardware clusters.
Rigmark — Measures local AI performance with reproducible, agent-like workloads.
Together AI — Fast serverless inference and GPU clusters with transparent token pricing.
RunThisLLM — Provides exact hardware and VRAM requirements for over 295 local AI models.
fal — Access 1,000+ generative models and high-performance serverless GPUs.
llmrun — Check which open models run on your specific GPU or Mac instantly.
Imp — Runs local models directly on NVIDIA Blackwell hardware using native 4-bit math.

All GPUStack alternatives