Comparison
ggrun vs exllama
Verdict
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; pick exllama if exLlama provides a memory-efficient implementation of the LLaMa model with support for quantized weights, primarily aimed at users with NVIDIA GPUs from the 30-series onwards.
Markdown twin · ggrun alternatives · exllama alternatives
GraphCanon updated Sep 20, 2026
Trust & integrity
| Signal | ggrun | exllama |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 20, 2026 · github_public_v1 | Dormant (1072d since push) As of Sep 7, 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 7, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 15, 2026 · osv@v1 | Published findings As of Jul 11, 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
- ggrun
- Auto-tuned launcher for GGUF models on llama.cpp with OpenAI-compatible server
- exllama
- Memory-efficient rewrite of HF transformers for Llama with quantized weights
Stars
- ggrun
- 275
- exllama
- 2.9k
Forks
- ggrun
- 18
- exllama
- 220
Open issues
- ggrun
- 4
- exllama
- 65
Language
- ggrun
- Go
- exllama
- Python
Adopt for
- 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.
- exllama
- ExLlama provides a memory-efficient implementation of the LLaMa model with support for quantized weights, primarily aimed at users with NVIDIA GPUs from the 30-series onwards.
Persona
- ggrun
- -
- exllama
- -
Runtime
- ggrun
- -
- exllama
- -
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.
- exllama
- MIT
Last pushed
- ggrun
- Sep 19, 2026
- exllama
- Sep 30, 2023
Categories
- ggrun
- Inference & Serving
- exllama
- Inference & Serving, LLM Frameworks
Trust and health
Maintenance
- ggrun
- Very active (96%)
- exllama
- Dormant (18%)
Days since push
- ggrun
- 0d
- exllama
- 1072d
Open issues (now)
- ggrun
- 4
- exllama
- 65
Stars delta
- ggrun
- +11 (30d)
- exllama
- +6 (30d)
Open issues delta
- ggrun
- +3 (30d)
- exllama
- 0 (30d)
OSV dependency advisories
- ggrun
- No lockfile (source not queried)
- exllama
- Published findings
Full report
- ggrun
- Trust report
- exllama
- Trust report
Choose ggrun if…
- ggrun is primarily Go; exllama is Python.
- 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
Choose exllama if…
- exllama is primarily Python; ggrun is Go.
- Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu.
- Also covers LLM Frameworks.
- exllama ships Docker support for self-hosted deployment.
- - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support.
When NOT to use exllama
- - If you are operating older GPUs such as Pascal series, which lack robust FP16 support; alternatives like AutoGPTQ might perform better.
- - In scenarios that involve AMD GPU hardware (due to limited testing and optimization efforts).
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- 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 (turboderp/exllama) · observed Sep 20, 2026
- GitHub forks (turboderp/exllama) · observed Sep 20, 2026
- Last push (turboderp/exllama) · observed Sep 30, 2023
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: ggrun 275 · exllama 2.9k (synced Sep 20, 2026).
Common questions
- What is the difference between ggrun and exllama?
- ggrun: Auto-tuned launcher for GGUF models on llama.cpp with OpenAI-compatible server. exllama: Memory-efficient rewrite of HF transformers for Llama with quantized weights. See the comparison table for live GitHub stats and shared categories.
- When should I choose ggrun over exllama?
- Choose ggrun over exllama when ggrun is primarily Go; exllama is Python; 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 choose exllama over ggrun?
- Choose exllama over ggrun when exllama is primarily Python; ggrun is Go; Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu; Also covers LLM Frameworks; exllama ships Docker support for self-hosted deployment; - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support.
- 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
- When should I avoid exllama?
- - If you are operating older GPUs such as Pascal series, which lack robust FP16 support; alternatives like AutoGPTQ might perform better. - In scenarios that involve AMD GPU hardware (due to limited testing and optimization efforts).
- Is ggrun or exllama more popular on GitHub?
- exllama has more GitHub stars (2,943 vs 275). Stars measure visibility, not whether either tool fits your constraints.
- Are ggrun and exllama open source?
- Yes - both are open-source projects on GitHub (ggrun: MIT, exllama: MIT).
- Where can I find alternatives to ggrun or exllama?
- GraphCanon lists graph-backed alternatives at ggrun alternatives and exllama alternatives (ggrun markdown twin, exllama 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, ggrun or exllama?
- ggrun: Very active. exllama: Dormant. 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 ggrun and exllama?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ggrun trust report; exllama trust report.