OpenLLM runs open-source language models as cloud API endpoints compatible with OpenAI clients.
Open source
21 alternativesGPUStack
GPUStack manages GPU clusters for serving AI models and provides on demand GPU instances accessible through SSH.
About GPUStack
GPUStack is an open-source GPU cluster manager for AI model serving and GPU instance provisioning. It configures and orchestrates inference engines such as vLLM, SGLang, and TensorRT-LLM, or a custom engine, and can launch SSH-accessible GPU instances on demand for development, fine-tuning, and interactive workloads. It supports industry-standard APIs for LLM, voice, image, and video models.
It manages GPU clusters across on-premises servers, Kubernetes clusters, and cloud providers, using a scheduler that allocates GPUs and selects inference engines. Operational features include automated failure recovery, load balancing, monitoring, authentication and access control, and metering of token usage and API request rates. A single server can manage multiple clusters. The repository notes that the GPU Cluster Topology view is a GPUStack Enterprise feature. Pricing is not stated.
Key features
- Multi-cluster GPU management across on-premises, Kubernetes, and cloud
- Pluggable engines: vLLM, SGLang, TensorRT-LLM, or custom
- On-demand SSH-accessible GPU instances for development and fine-tuning
- Pre-tuned modes for low latency or high throughput
- Built-in authentication, access control, and GPU monitoring
- Supports NVIDIA, AMD, Ascend NPU, Hygon DCU, and other accelerators
Best for: Development teams, IT organizations, and service providers that need to run and operate model serving across their own GPU clusters or provision GPU instances on demand.
Good for
- Open-source project with a public repository
- Pluggable engine architecture allows deploying new models on release day
- Includes operational tooling such as failure recovery, load balancing, monitoring, and access control
Watch out for
- Quick start requires a node with at least one NVIDIA GPU plus the NVIDIA driver, Docker, and NVIDIA Container Toolkit
- Some capabilities shown, such as the GPU Cluster Topology view, are described as GPUStack Enterprise features
GPUStack at a glance
- Website
- github.com/gpustack/gpustack
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
GPUStack alternatives
Tools and sites you could use instead, ranked by how closely they match.
NobodyWho runs language models locally through an inference engine designed for efficient operation across devices.
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NVIDIA Triton Inference Server provides an inference solution for deploying models in cloud and edge environments.
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Sonar provides large-scale inference for language models through the project hosted in this repository.
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Jina Serve helps developers build multimodal AI applications with cloud-native tools.
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For Apple Silicon, Rapid-MLX offers an open-source LLM inference server and Mac app compatible with OpenAI and Anthropic APIs.
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GPUStack FAQ
What does a worker node need?
The quick start calls for a node with at least one NVIDIA GPU, with the NVIDIA driver, Docker, and the NVIDIA Container Toolkit installed. For other GPU types, the installation documentation and the GPUStack UI guidelines apply.
Which environments can GPUStack manage?
On-premises servers, Kubernetes clusters, and cloud providers, with a single GPUStack server able to manage multiple GPU clusters.
Can I use my own inference engine?
Yes. The engine architecture is pluggable: alongside vLLM, SGLang, and TensorRT-LLM, custom inference engines can be added as needed.
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