Comparison
awesome-local-llm vs ggrun
Verdict
Pick awesome-local-llm if awesome-local-llm is a curated list of resources for the local operation of large language models; 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 · awesome-local-llm alternatives · ggrun alternatives
GraphCanon updated Sep 20, 2026
5views this month
vs
Trust & integrity
| Signal | awesome-local-llm | ggrun |
|---|---|---|
| Maintenance | Very active (6d since push) As of Sep 20, 2026 · github_public_v1 | Very active (0d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 15, 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
- awesome-local-llm
- Resources for running LLMs locally
- ggrun
- Auto-tuned launcher for GGUF models on llama.cpp with OpenAI-compatible server
Stars
- awesome-local-llm
- 2.9k
- ggrun
- 275
Forks
- awesome-local-llm
- 388
- ggrun
- 18
Open issues
- awesome-local-llm
- 169
- ggrun
- 4
Language
- awesome-local-llm
- -
- ggrun
- Go
Adopt for
- awesome-local-llm
- awesome-local-llm is a curated list of resources for the local operation of large language models.
- 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
- awesome-local-llm
- -
- ggrun
- -
Runtime
- awesome-local-llm
- -
- ggrun
- -
License
- awesome-local-llm
- MIT License
- 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
- awesome-local-llm
- Sep 13, 2026
- ggrun
- Sep 19, 2026
Categories
- awesome-local-llm
- Inference & Serving
- ggrun
- Inference & Serving
Trust and health
Days since push
- awesome-local-llm
- 6d
- ggrun
- 0d
Open issues (now)
- awesome-local-llm
- 169
- ggrun
- 4
Stars delta
- awesome-local-llm
- +351 (30d)
- ggrun
- +11 (30d)
Open issues delta
- awesome-local-llm
- +40 (30d)
- ggrun
- +3 (30d)
Full report
- awesome-local-llm
- Trust report
- ggrun
- Trust report
Choose awesome-local-llm if…
- Pricing: The list itself is free and open-source under the MIT license..
- Requirements: Technical skill in setting up a self-hosted large language model environment is necessary.
- Tags unique to awesome-local-llm: ai, awesome-list, local-ai, self-hosted.
- - If you require extensive documentation and resources for setting up and running LLMs on your own hardware, this tool provides a comprehensive list of options
When NOT to use awesome-local-llm
- - Avoid if you seek direct tools rather than a curated list; awesome-local-llm does not provide the actual software but guidance and links
- - Not suitable for users who prefer ready-to-use solutions without needing additional configuration, as it requires self-hosting expertise to utilize its resources
Choose ggrun if…
- Pricing: Free to use under MIT license; no direct costs involved in usage..
- Tags unique to ggrun: cuda, gguf, golang, inference-server.
- 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 (rafska/awesome-local-llm) · observed Sep 20, 2026
- GitHub forks (rafska/awesome-local-llm) · observed Sep 20, 2026
- Last push (rafska/awesome-local-llm) · observed Sep 13, 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 (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: awesome-local-llm 2.9k · ggrun 275 (synced Sep 20, 2026).
Common questions
- What is the difference between awesome-local-llm and ggrun?
- awesome-local-llm: Resources for running LLMs locally. 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 awesome-local-llm over ggrun?
- Choose awesome-local-llm over ggrun when Pricing: The list itself is free and open-source under the MIT license.; Requirements: Technical skill in setting up a self-hosted large language model environment is necessary; Tags unique to awesome-local-llm: ai, awesome-list, local-ai, self-hosted; - If you require extensive documentation and resources for setting up and running LLMs on your own hardware, this tool provides a comprehensive list of options.
- When should I choose ggrun over awesome-local-llm?
- Choose ggrun over awesome-local-llm when Pricing: Free to use under MIT license; no direct costs involved in usage.; Tags unique to ggrun: cuda, gguf, golang, inference-server; When developing systems that require automatic hardware optimization and tuning for GGUF models on multiple GPUs.
- When should I avoid awesome-local-llm?
- - Avoid if you seek direct tools rather than a curated list; awesome-local-llm does not provide the actual software but guidance and links - Not suitable for users who prefer ready-to-use solutions without needing additional configuration, as it requires self-hosting expertise to utilize its resources
- 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 awesome-local-llm or ggrun more popular on GitHub?
- awesome-local-llm has more GitHub stars (2,869 vs 275). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-local-llm and ggrun open source?
- Yes - both are open-source projects on GitHub (awesome-local-llm: MIT, ggrun: MIT).
- Where can I find alternatives to awesome-local-llm or ggrun?
- GraphCanon lists graph-backed alternatives at awesome-local-llm alternatives and ggrun alternatives (awesome-local-llm 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, awesome-local-llm or ggrun?
- awesome-local-llm: 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 awesome-local-llm and ggrun?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-local-llm trust report; ggrun trust report.