LLMKube vs gpustack

Common questions

What is the difference between LLMKube and gpustack?

LLMKube: Kubernetes operator for self-hosted LLM inference across heterogeneous GPU fleets. gpustack: A GPU cluster manager for high-performance AI model serving and on-demand SSH-accessible GPU instances. See the comparison table for live GitHub stats and shared categories.

When should I choose LLMKube over gpustack?

Choose LLMKube over gpustack when LLMKube is primarily Go; gpustack is Python; Pricing: Free and open-source under the Apache-2.0 license.; Requirements: Requires a Kubernetes cluster for installation and operation.; Supports deployment on a variety of GPU architectures, including NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal.; Tags unique to LLMKube: ai, apple-silicon, autoscaling, edge-computing; LLMKube ships Docker support for self-hosted deployment; When you need to deploy and manage self-hosted large language model inference services across a variety of GPU architectures, including NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal.

When should I choose gpustack over LLMKube?

Choose gpustack over LLMKube when gpustack is primarily Python; LLMKube is Go; Pricing: gpustack is free to use under the Apache License, Version 2.0, but users may incur costs related to the infrastructure and resources used to run the tool.; Requirements: Min 4 GB RAM; Requires Docker; gpustack requires Docker for installation and management.; Ensure that the Docker environment is properly set up and that the user has the necessary permissions to run Docker commands.; Tags unique to gpustack: ascend, cuda, deepseek, distributed-inference; When you need to manage a GPU cluster for high-performance AI model serving and require on-demand SSH-accessible GPU instances.

When should I avoid LLMKube?

If your infrastructure does not include a Kubernetes cluster, as LLMKube is designed to operate within a Kubernetes environment. If you are looking for a solution that does not support a heterogeneous mix of GPU architectures, LLMKube is specifically designed to work with NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. If you do not require or prefer not to use the specific runtimes supported by LLMKube, such as llama.cpp, vLLM, TGI, and mlx-server.

When should I avoid gpustack?

If your project does not require the specific AI frameworks and technologies supported by gpustack, such as vLLM and SGLang, and you need broader support. When you are looking for a solution that does not rely on Docker for deployment, as gpustack requires Docker for installation and management. If you need a tool that provides more extensive customization options beyond what gpustack offers, as it may not be as flexible in terms of configuration.

Is LLMKube or gpustack more popular on GitHub?

gpustack has more GitHub stars (5,710 vs 210). Stars measure visibility, not whether either tool fits your constraints.

Are LLMKube and gpustack open source?

Yes - both are open-source projects on GitHub (LLMKube: Apache-2.0, gpustack: Apache-2.0).

Where can I find alternatives to LLMKube or gpustack?

GraphCanon lists graph-backed alternatives at LLMKube alternatives and gpustack alternatives (LLMKube markdown twin, gpustack markdown twin), ranked by typed relationship edges rather than popularity votes.

Is there a machine-readable version of this comparison?

Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

Which is better maintained, LLMKube or gpustack?

LLMKube: Very active. gpustack: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

Where are the full trust reports for LLMKube and gpustack?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLMKube trust report; gpustack trust report.