--- title: "aikit vs exllama" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kaito-project-aikit-vs-turboderp-exllama" tools: ["kaito-project-aikit", "turboderp-exllama"] --- # aikit vs exllama *GraphCanon updated Jul 12, 2026* ## Verdict Pick aikit when aikit is primarily Go; exllama is Python; pick exllama when exllama is primarily Python; aikit is Go. [aikit](https://kaito-project.github.io/aikit/) reports 533 GitHub stars, 57 forks, and 41 open issues, last pushed Jul 11, 2026. [exllama](https://github.com/turboderp/exllama) has 2.9k stars, 223 forks, and 65 open issues, last pushed Sep 30, 2023. Figures are from public GitHub metadata via [aikit's repository](https://github.com/kaito-project/aikit) and [exllama's repository](https://github.com/turboderp/exllama). | | [aikit](/tools/kaito-project-aikit.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Tagline | Fine-tune, build, and deploy open-source LLMs easily! | More memory-efficient rewrite of HF transformers for Llama with quantized weights | | Stars | 533 | 2,930 | | Forks | 57 | 223 | | Open issues | 41 | 65 | | Language | Go | Python | | Adopt for | Aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies. | - | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Inference & Serving, LLM Frameworks, Model Training | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [aikit](/tools/kaito-project-aikit.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 1014d | | Open issues (now) | 41 | 65 | | Owner type | Organization | User | | Security scan | No lockfile | 29 low (29 low) | | Full report | [trust report](/tools/kaito-project-aikit/trust.md) | [trust report](/tools/turboderp-exllama/trust.md) | ## Decision facts: aikit - **Adopt for:** Aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies. ## Choose when ### Choose aikit if… - aikit is primarily Go; exllama is Python. - Tags unique to aikit: ai, buildkit, chatgpt, docker. - Also covers Model Training. - - You need a flexible solution specifically built using Go and prefer its concurrency model. ### Choose exllama if… - exllama is primarily Python; aikit is Go. - Tags unique to exllama: docker container support, gpu optimization, memory efficiency, nvidia support. - More GitHub stars (2.9k vs 533) - visibility, not fit. ## 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. ## When NOT to use exllama - Last GitHub push was 1015 days ago (dormant maintenance, Sep 30, 2023). Validate activity before betting a new project on exllama. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## Common questions ### What is the difference between aikit and exllama? aikit: Fine-tune, build, and deploy open-source LLMs easily!. exllama: More 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, docker; 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: docker container support, gpu optimization, memory efficiency, nvidia support; More GitHub stars (2.9k vs 533) - visibility, not fit. ### 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? Last GitHub push was 1015 days ago (dormant maintenance, Sep 30, 2023). Validate activity before betting a new project on exllama. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### Is aikit or exllama more popular on GitHub? exllama has more GitHub stars (2,930 vs 533). 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](/tools/kaito-project-aikit/alternatives) and [exllama alternatives](/tools/turboderp-exllama/alternatives) ([aikit markdown twin](/tools/kaito-project-aikit/alternatives.md), [exllama markdown twin](/tools/turboderp-exllama/alternatives.md)), 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](/compare/kaito-project-aikit-vs-turboderp-exllama.md) 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](/tools/kaito-project-aikit/trust); [exllama trust report](/tools/turboderp-exllama/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kaito-project-aikit`](/api/graphcanon/graph?tool=kaito-project-aikit) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_