Comparison
aikit vs exllama
Verdict
Pick aikit if aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies; 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 · aikit alternatives · exllama alternatives
GraphCanon updated Sep 20, 2026
15views this month
Trust & integrity
| Signal | aikit | exllama |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 19, 2026 · github_public_v1 | Dormant (1072d since push) As of Sep 7, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 19, 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 11, 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
- aikit
- Fine-tune, build, and deploy open-source LLMs easily!
- exllama
- Memory-efficient rewrite of HF transformers for Llama with quantized weights
Stars
- aikit
- 539
- exllama
- 2.9k
Forks
- aikit
- 57
- exllama
- 220
Open issues
- aikit
- 37
- exllama
- 65
Language
- aikit
- Go
- exllama
- Python
Adopt for
- aikit
- Aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies.
- 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
- aikit
- -
- exllama
- -
Runtime
- aikit
- -
- exllama
- -
License
- aikit
- MIT
- exllama
- MIT
Last pushed
- aikit
- Sep 18, 2026
- exllama
- Sep 30, 2023
Categories
- aikit
- Inference & Serving, LLM Frameworks, Model Training
- exllama
- Inference & Serving, LLM Frameworks
Trust and health
Maintenance
- aikit
- Very active (96%)
- exllama
- Dormant (18%)
Days since push
- aikit
- 0d
- exllama
- 1072d
Open issues (now)
- aikit
- 37
- exllama
- 65
Stars delta
- aikit
- +5 (30d)
- exllama
- +6 (30d)
Open issues delta
- aikit
- -6 (30d)
- exllama
- 0 (30d)
Owner type
- aikit
- Organization
- exllama
- User
OSV dependency advisories
- aikit
- No lockfile (source not queried)
- exllama
- Published findings
Full report
- aikit
- Trust report
- exllama
- Trust report
Choose aikit if…
- aikit is primarily Go; exllama is Python.
- Tags unique to aikit: ai, buildkit, chatgpt, fine-tuning.
- Also covers Model Training.
- - You need a flexible solution specifically built using Go and prefer its concurrency model.
When NOT to use aikit
- - You have a preference or requirement for Python-based tools due to the lack of native support in Aikit.
- - If your deployment setup strictly uses cloud-specific platforms and you do not use Kubernetes or Docker, as Aikit heavily integrates with containerized environments like these.
Choose exllama if…
- exllama is primarily Python; aikit is Go.
- Tags unique to exllama: llama model, memory-efficient, nvidia gpu, python.
- - 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 (kaito-project/aikit) · observed Sep 20, 2026
- GitHub forks (kaito-project/aikit) · observed Sep 20, 2026
- Last push (kaito-project/aikit) · observed Sep 18, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 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: aikit 539 · exllama 2.9k (synced Sep 20, 2026).
Common questions
- What is the difference between aikit and exllama?
- aikit: Fine-tune, build, and deploy open-source LLMs easily!. 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 aikit over exllama?
- Choose aikit over exllama when aikit is primarily Go; exllama is Python; Tags unique to aikit: ai, buildkit, chatgpt, fine-tuning; Also covers Model Training; - You need a flexible solution specifically built using Go and prefer its concurrency model.
- When should I choose exllama over aikit?
- Choose exllama over aikit when exllama is primarily Python; aikit is Go; Tags unique to exllama: llama model, memory-efficient, nvidia gpu, python; - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support.
- When should I avoid aikit?
- - You have a preference or requirement for Python-based tools due to the lack of native support in Aikit. - If your deployment setup strictly uses cloud-specific platforms and you do not use Kubernetes or Docker, as Aikit heavily integrates with containerized environments like these.
- 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 aikit or exllama more popular on GitHub?
- exllama has more GitHub stars (2,943 vs 539). Stars measure visibility, not whether either tool fits your constraints.
- Are aikit and exllama open source?
- Yes - both are open-source projects on GitHub (aikit: MIT, exllama: MIT).
- Where can I find alternatives to aikit or exllama?
- GraphCanon lists graph-backed alternatives at aikit alternatives and exllama alternatives (aikit 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, aikit or exllama?
- aikit: 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 aikit and exllama?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: aikit trust report; exllama trust report.