Comparison
LLMKube vs ggrun
Verdict
Pick LLMKube if lLMKube is a Kubernetes operator for deploying and managing self-hosted large language model inference services on various GPU architectures, including NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. It supports runtim; pick ggrun if ggrun, an auto-tuned launcher for GGUF models using llama.cpp, offers OpenAI-compatible server support with multi-GPU tensor-split and MoE expert placement capabilities.
Markdown twin · LLMKube alternatives · ggrun alternatives
GraphCanon updated Sep 20, 2026
10views this month
Trust & integrity
| Signal | LLMKube | ggrun |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 18, 2026 · github_public_v1 | Very active (0d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Sep 18, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 15, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- LLMKube
- Kubernetes operator for self-hosted LLM inference across heterogeneous GPU fleets
- ggrun
- Auto-tuned launcher for GGUF models on llama.cpp with OpenAI-compatible server
Stars
- LLMKube
- 210
- ggrun
- 275
Forks
- LLMKube
- 33
- ggrun
- 18
Open issues
- LLMKube
- 98
- ggrun
- 4
Language
- LLMKube
- Go
- ggrun
- Go
Adopt for
- LLMKube
- LLMKube is a Kubernetes operator for deploying and managing self-hosted large language model inference services on various GPU architectures, including NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. It supports runtim
- ggrun
- ggrun, an auto-tuned launcher for GGUF models using llama.cpp, offers OpenAI-compatible server support with multi-GPU tensor-split and MoE expert placement capabilities.
Persona
- LLMKube
- -
- ggrun
- -
Runtime
- LLMKube
- -
- ggrun
- -
License
- LLMKube
- Apache-2.0
- ggrun
- MIT License allows using ggrun freely in both open source and commercial projects, with conditions that the copyright notice and permission notice are preserved.
Last pushed
- LLMKube
- Sep 17, 2026
- ggrun
- Sep 19, 2026
Categories
- LLMKube
- Inference & Serving
- ggrun
- Inference & Serving
Trust and health
Open issues (now)
- LLMKube
- 98
- ggrun
- 4
Stars delta
- LLMKube
- +27 (30d)
- ggrun
- +11 (30d)
Open issues delta
- LLMKube
- +21 (30d)
- ggrun
- +3 (30d)
Owner type
- LLMKube
- Organization
- ggrun
- User
OSV dependency advisories
- LLMKube
- Published findings
- ggrun
- No lockfile (source not queried)
Full report
- LLMKube
- Trust report
- ggrun
- Trust report
Choose LLMKube if…
- License: LLMKube is Apache-2.0, ggrun is MIT.
- 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 NOT to use 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.
Choose ggrun if…
- License: ggrun is MIT, LLMKube is Apache-2.0.
- Pricing: Free to use under MIT license; no direct costs involved in usage..
- Tags unique to ggrun: cuda, golang, inference-server, llama-cpp.
- When developing systems that require automatic hardware optimization and tuning for GGUF models on multiple GPUs
When NOT to use ggrun
- For environments where single-GPU setups are preferred, as ggrun specializes in multi-GPU configurations and may offer limited advantage or additional complexity
- When you do not require auto-tuning capabilities for hardware performance optimization since this feature is specific to ggrun
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (defilantech/LLMKube) · observed Sep 20, 2026
- GitHub forks (defilantech/LLMKube) · observed Sep 20, 2026
- Last push (defilantech/LLMKube) · observed Sep 17, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
- GitHub stars (raketenkater/ggrun) · observed Sep 20, 2026
- GitHub forks (raketenkater/ggrun) · observed Sep 20, 2026
- Last push (raketenkater/ggrun) · observed Sep 19, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: LLMKube 210 · ggrun 275 (synced Sep 20, 2026).
Common questions
- What is the difference between LLMKube and ggrun?
- LLMKube: Kubernetes operator for self-hosted LLM inference across heterogeneous GPU fleets. ggrun: Auto-tuned launcher for GGUF models on llama.cpp with OpenAI-compatible server. See the comparison table for live GitHub stats and shared categories.
- When should I choose LLMKube over ggrun?
- Choose LLMKube over ggrun when License: LLMKube is Apache-2.0, ggrun is MIT; 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 ggrun over LLMKube?
- Choose ggrun over LLMKube when License: ggrun is MIT, LLMKube is Apache-2.0; Pricing: Free to use under MIT license; no direct costs involved in usage.; Tags unique to ggrun: cuda, golang, inference-server, llama-cpp; When developing systems that require automatic hardware optimization and tuning for GGUF models on multiple GPUs.
- 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 ggrun?
- For environments where single-GPU setups are preferred, as ggrun specializes in multi-GPU configurations and may offer limited advantage or additional complexity When you do not require auto-tuning capabilities for hardware performance optimization since this feature is specific to ggrun
- Is LLMKube or ggrun more popular on GitHub?
- ggrun has more GitHub stars (275 vs 210). Stars measure visibility, not whether either tool fits your constraints.
- Are LLMKube and ggrun open source?
- Yes - both are open-source projects on GitHub (LLMKube: Apache-2.0, ggrun: MIT).
- Where can I find alternatives to LLMKube or ggrun?
- GraphCanon lists graph-backed alternatives at LLMKube alternatives and ggrun alternatives (LLMKube markdown twin, ggrun 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 ggrun?
- LLMKube: Very active. ggrun: 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 ggrun?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLMKube trust report; ggrun trust report.