--- title: "aikit vs FasterTransformer" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kaito-project-aikit-vs-nvidia-fastertransformer" tools: ["kaito-project-aikit", "nvidia-fastertransformer"] --- # aikit vs FasterTransformer *GraphCanon updated Aug 24, 2026* ## 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 FasterTransformer if highly optimized transformer encoder and decoder for inferencing, supporting BERT and GPT on various frameworks like TensorFlow, PyTorch. [aikit](https://kaito-project.github.io/aikit/) reports 537 GitHub stars, 57 forks, and 40 open issues, last pushed Aug 24, 2026. [FasterTransformer](https://github.com/NVIDIA/FasterTransformer) has 6.4k stars, 935 forks, and 289 open issues, last pushed Mar 27, 2024. Figures are from public GitHub metadata via [aikit's repository](https://github.com/kaito-project/aikit) and [FasterTransformer's repository](https://github.com/NVIDIA/FasterTransformer). | | [aikit](/tools/kaito-project-aikit.md) | [FasterTransformer](/tools/nvidia-fastertransformer.md) | | --- | --- | --- | | Tagline | Fine-tune, build, and deploy open-source LLMs easily! | Transformer related optimization including BERT and GPT | | Stars | 537 | 6,446 | | Forks | 57 | 935 | | Open issues | 40 | 289 | | Language | Go | C++ | | Adopt for | Aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies. | Highly optimized transformer encoder and decoder for inferencing, supporting BERT and GPT on various frameworks like TensorFlow, PyTorch. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Inference & Serving, LLM Frameworks, Model Training | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [aikit](/tools/kaito-project-aikit.md) | [FasterTransformer](/tools/nvidia-fastertransformer.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 862d | | Open issues (now) | 40 | 289 | | Stars delta | +3 (30d) | Unknown | | Open issues delta | -3 (30d) | Unknown | | Full report | [trust report](/tools/kaito-project-aikit/trust.md) | [trust report](/tools/nvidia-fastertransformer/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. ## Decision facts: FasterTransformer - **Adopt for:** Highly optimized transformer encoder and decoder for inferencing, supporting BERT and GPT on various frameworks like TensorFlow, PyTorch. ## Choose when ### Choose aikit if… - aikit is primarily Go; FasterTransformer is C++. - License: aikit is MIT, FasterTransformer is Apache-2.0. - Tags unique to aikit: ai, buildkit, chatgpt, docker. - Also covers LLM Frameworks, Model Training. - aikit ships Docker support for self-hosted deployment. - - You need a flexible solution specifically built using Go and prefer its concurrency model. ### Choose FasterTransformer if… - FasterTransformer is primarily C++; aikit is Go. - License: FasterTransformer is Apache-2.0, aikit is MIT. - Tags unique to FasterTransformer: bert, cublas, cublaslt, cuda. - When aiming for high performance with GPU-based FP16 computations for BERT or GPT models specifically. ## 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 FasterTransformer - If looking for active development and latest improvements on LLM Inference as NVIDIA recommends TensorRT-LLM over FasterTransformer now. - When specific frameworks not including TensorFlow, PyTorch, or Triton are required. ## Common questions ### What is the difference between aikit and FasterTransformer? aikit: Fine-tune, build, and deploy open-source LLMs easily!. FasterTransformer: Transformer related optimization including BERT and GPT. See the comparison table for live GitHub stats and shared categories. ### When should I choose aikit over FasterTransformer? Choose aikit over FasterTransformer when aikit is primarily Go; FasterTransformer is C++; License: aikit is MIT, FasterTransformer is Apache-2.0; Tags unique to aikit: ai, buildkit, chatgpt, docker; Also covers LLM Frameworks, Model Training; aikit ships Docker support for self-hosted deployment; - You need a flexible solution specifically built using Go and prefer its concurrency model. ### When should I choose FasterTransformer over aikit? Choose FasterTransformer over aikit when FasterTransformer is primarily C++; aikit is Go; License: FasterTransformer is Apache-2.0, aikit is MIT; Tags unique to FasterTransformer: bert, cublas, cublaslt, cuda; When aiming for high performance with GPU-based FP16 computations for BERT or GPT models specifically. ### 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 FasterTransformer? If looking for active development and latest improvements on LLM Inference as NVIDIA recommends TensorRT-LLM over FasterTransformer now. When specific frameworks not including TensorFlow, PyTorch, or Triton are required. ### Is aikit or FasterTransformer more popular on GitHub? FasterTransformer has more GitHub stars (6,446 vs 537). Stars measure visibility, not whether either tool fits your constraints. ### Are aikit and FasterTransformer open source? Yes - both are open-source projects on GitHub (aikit: MIT, FasterTransformer: Apache-2.0). ### Where can I find alternatives to aikit or FasterTransformer? GraphCanon lists graph-backed alternatives at [aikit alternatives](/tools/kaito-project-aikit/alternatives) and [FasterTransformer alternatives](/tools/nvidia-fastertransformer/alternatives) ([aikit markdown twin](/tools/kaito-project-aikit/alternatives.md), [FasterTransformer markdown twin](/tools/nvidia-fastertransformer/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-nvidia-fastertransformer.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, aikit or FasterTransformer? aikit: Very active. FasterTransformer: 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 FasterTransformer? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [aikit trust report](/tools/kaito-project-aikit/trust); [FasterTransformer trust report](/tools/nvidia-fastertransformer/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/_