Comparison
distributed-llama vs ggrun
Verdict
Pick distributed-llama if distributed-llama is a C++ framework that leverages multiple home devices for faster large language model inference, under the MIT license; 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 · distributed-llama alternatives · ggrun alternatives
GraphCanon updated Aug 24, 2026
vs
Trust & integrity
| Signal | distributed-llama | ggrun |
|---|---|---|
| Maintenance | Steady (50d since push) As of Aug 24, 2026 · github_public_v1 | Very active (1d since push) As of Aug 13, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Aug 24, 2026 · github_public_v1 | Not a fork · Personal account As of Aug 13, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 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
- distributed-llama
- Distributed LLM inference using home devices cluster
- ggrun
- Auto-tuned launcher for GGUF models on llama.cpp with OpenAI-compatible server
Stars
- distributed-llama
- 3.0k
- ggrun
- 264
Forks
- distributed-llama
- 246
- ggrun
- 14
Open issues
- distributed-llama
- 48
- ggrun
- 1
Language
- distributed-llama
- C++
- ggrun
- Go
Adopt for
- distributed-llama
- distributed-llama is a C++ framework that leverages multiple home devices for faster large language model inference, under the MIT license.
- 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
- distributed-llama
- -
- ggrun
- -
Runtime
- distributed-llama
- -
- ggrun
- -
License
- distributed-llama
- MIT
- 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
- distributed-llama
- Jul 5, 2026
- ggrun
- Aug 11, 2026
Categories
- distributed-llama
- Inference & Serving
- ggrun
- Inference & Serving
Trust and health
Maintenance
- distributed-llama
- Steady (60%)
- ggrun
- Very active (96%)
Days since push
- distributed-llama
- 50d
- ggrun
- 1d
Open issues (now)
- distributed-llama
- 48
- ggrun
- 1
Stars delta
- distributed-llama
- +32 (30d)
- ggrun
- Unknown
Open issues delta
- distributed-llama
- 0 (30d)
- ggrun
- Unknown
Full report
- distributed-llama
- Trust report
- ggrun
- Trust report
Choose distributed-llama if…
- distributed-llama is primarily C++; ggrun is Go.
- Tags unique to distributed-llama: distributed-computing, llm-inference, neural-network.
- When you have multiple interconnected home devices and want to maximize their combined computing power for LLM inference tasks.
When NOT to use distributed-llama
- For scenarios with fewer than two available devices, as the framework's capability to distribute and boost performance would be limited.
- In professional environments that require strict data privacy controls, due to potential network vulnerabilities among home devices.
Choose ggrun if…
- ggrun is primarily Go; distributed-llama is C++.
- 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 (b4rtaz/distributed-llama) · observed Aug 24, 2026
- GitHub forks (b4rtaz/distributed-llama) · observed Aug 24, 2026
- Last push (b4rtaz/distributed-llama) · observed Jul 5, 2026
- License file (MIT) · observed Aug 24, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (raketenkater/ggrun) · observed Aug 13, 2026
- GitHub forks (raketenkater/ggrun) · observed Aug 13, 2026
- Last push (raketenkater/ggrun) · observed Aug 11, 2026
- License file (MIT) · observed Aug 13, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: distributed-llama 3.0k · ggrun 264 (synced Aug 24, 2026).
Common questions
- What is the difference between distributed-llama and ggrun?
- distributed-llama: Distributed LLM inference using home devices cluster. 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 distributed-llama over ggrun?
- Choose distributed-llama over ggrun when distributed-llama is primarily C++; ggrun is Go; Tags unique to distributed-llama: distributed-computing, llm-inference, neural-network; When you have multiple interconnected home devices and want to maximize their combined computing power for LLM inference tasks.
- When should I choose ggrun over distributed-llama?
- Choose ggrun over distributed-llama when ggrun is primarily Go; distributed-llama is C++; 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 distributed-llama?
- For scenarios with fewer than two available devices, as the framework's capability to distribute and boost performance would be limited. In professional environments that require strict data privacy controls, due to potential network vulnerabilities among home devices.
- 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 distributed-llama or ggrun more popular on GitHub?
- distributed-llama has more GitHub stars (3,044 vs 264). Stars measure visibility, not whether either tool fits your constraints.
- Are distributed-llama and ggrun open source?
- Yes - both are open-source projects on GitHub (distributed-llama: MIT, ggrun: MIT).
- Where can I find alternatives to distributed-llama or ggrun?
- GraphCanon lists graph-backed alternatives at distributed-llama alternatives and ggrun alternatives (distributed-llama 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, distributed-llama or ggrun?
- distributed-llama: Steady. 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 distributed-llama and ggrun?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: distributed-llama trust report; ggrun trust report.