Comparison
aikit vs FasterTransformer
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.
Markdown twin · aikit alternatives · FasterTransformer alternatives
GraphCanon updated 1d
vs
Trust & integrity
| Signal | aikit | FasterTransformer |
|---|---|---|
| Maintenance | Very active (0d since push) As of 1d · github_public_v1 | Dormant (862d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1d · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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!
- FasterTransformer
- Transformer related optimization including BERT and GPT
Stars
- aikit
- 537
- FasterTransformer
- 6.4k
Forks
- aikit
- 57
- FasterTransformer
- 935
Open issues
- aikit
- 40
- FasterTransformer
- 289
Language
- aikit
- Go
- FasterTransformer
- C++
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.
- FasterTransformer
- Highly optimized transformer encoder and decoder for inferencing, supporting BERT and GPT on various frameworks like TensorFlow, PyTorch.
Persona
- aikit
- -
- FasterTransformer
- -
Runtime
- aikit
- -
- FasterTransformer
- -
License
- aikit
- MIT
- FasterTransformer
- Apache-2.0
Last pushed
- aikit
- Aug 24, 2026
- FasterTransformer
- Mar 27, 2024
Categories
- aikit
- Inference & Serving, LLM Frameworks, Model Training
- FasterTransformer
- Inference & Serving
Trust and health
Maintenance
- aikit
- Very active (96%)
- FasterTransformer
- Dormant (18%)
Days since push
- aikit
- 0d
- FasterTransformer
- 862d
Open issues (now)
- aikit
- 40
- FasterTransformer
- 289
Stars delta
- aikit
- +3 (30d)
- FasterTransformer
- Unknown
Open issues delta
- aikit
- -3 (30d)
- FasterTransformer
- Unknown
Full report
- aikit
- Trust report
- FasterTransformer
- Trust report
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.
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 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 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.
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 Aug 24, 2026
- GitHub forks (kaito-project/aikit) · observed Aug 24, 2026
- Last push (kaito-project/aikit) · observed Aug 24, 2026
- License file (MIT) · observed Aug 24, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (NVIDIA/FasterTransformer) · observed Aug 7, 2026
- GitHub forks (NVIDIA/FasterTransformer) · observed Aug 7, 2026
- Last push (NVIDIA/FasterTransformer) · observed Mar 27, 2024
- License file (Apache-2.0) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: aikit 537 · FasterTransformer 6.4k (synced Aug 24, 2026).
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 and FasterTransformer alternatives (aikit markdown twin, FasterTransformer 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 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; FasterTransformer trust report.