Comparison
litgpt vs TransformerEngine
Verdict
Pick litgpt if litgpt offers a suite of over 20 high-performance large language models, with tools for pretraining, finetuning, and deployment at scale, all under the Apache-2.0 license; pick TransformerEngine if transformerEngine optimizes Transformer model performance with FP8/FP4 precision on NVIDIA GPUs like Hopper, Ada, and Blackwell, boosting throughput and reducing memory usage.
Markdown twin · litgpt alternatives · TransformerEngine alternatives
GraphCanon updated Sep 20, 2026
12views this month
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Trust & integrity
| Signal | litgpt | TransformerEngine |
|---|---|---|
| Maintenance | Very active (3d since push) As of Sep 18, 2026 · github_public_v1 | Very active (1d since push) As of Sep 7, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 7, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Sep 18, 2026 · osv@v1 | No lockfile (source not queried) 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
- litgpt
- High-performance LLMs for pretraining, finetuning, and deployment
- TransformerEngine
- A library for accelerating Transformer models on NVIDIA GPUs using low precision formats like FP8 and FP4.
Stars
- litgpt
- 14k
- TransformerEngine
- 3.5k
Forks
- litgpt
- 1.5k
- TransformerEngine
- 819
Open issues
- litgpt
- 290
- TransformerEngine
- 343
Language
- litgpt
- Python
- TransformerEngine
- Python
Adopt for
- litgpt
- litgpt offers a suite of over 20 high-performance large language models, with tools for pretraining, finetuning, and deployment at scale, all under the Apache-2.0 license.
- TransformerEngine
- TransformerEngine optimizes Transformer model performance with FP8/FP4 precision on NVIDIA GPUs like Hopper, Ada, and Blackwell, boosting throughput and reducing memory usage.
Persona
- litgpt
- -
- TransformerEngine
- -
Runtime
- litgpt
- -
- TransformerEngine
- -
License
- litgpt
- Apache-2.0
- TransformerEngine
- Apache-2.0
Last pushed
- litgpt
- Sep 14, 2026
- TransformerEngine
- Sep 5, 2026
Categories
- litgpt
- Inference & Serving, LLM Frameworks, Model Training
- TransformerEngine
- Inference & Serving, Model Training
Trust and health
Days since push
- litgpt
- 3d
- TransformerEngine
- 1d
Open issues (now)
- litgpt
- 290
- TransformerEngine
- 343
Stars delta
- litgpt
- +62 (30d)
- TransformerEngine
- +45 (30d)
Open issues delta
- litgpt
- +18 (30d)
- TransformerEngine
- +33 (30d)
Full report
- litgpt
- Trust report
- TransformerEngine
- Trust report
Choose litgpt if…
- Tags unique to litgpt: ai, artificial-intelligence, large-language-models, llm.
- Also covers LLM Frameworks.
- When you need a comprehensive set of over 20 high-performance LLMs for pretraining, finetuning, and deployment.
When NOT to use litgpt
- If you are looking for a tool that supports languages other than Python, as litgpt is exclusively Python-based.
- When you need a tool that offers fewer model options, as litgpt provides over 20 models which might be overwhelming for specific use cases.
- If you require proprietary licensing, as litgpt is open-source under the Apache-2.0 license.
Choose TransformerEngine if…
- Tags unique to TransformerEngine: cuda, fp4, fp8, gpu.
- If you need high-throughput training or inference of Transformer models specifically on compatible NVIDIA GPUs (Hopper, Ada, Blackwell).
When NOT to use TransformerEngine
- Avoid if your project is not running on NVIDIA's Hopper, Ada, or Blackwell GPUs.
- If memory usage isn't a critical concern and you prefer higher precision over speed optimization.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Lightning-AI/litgpt) · observed Sep 20, 2026
- GitHub forks (Lightning-AI/litgpt) · observed Sep 20, 2026
- Last push (Lightning-AI/litgpt) · observed Sep 14, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
- GitHub stars (NVIDIA/TransformerEngine) · observed Sep 20, 2026
- GitHub forks (NVIDIA/TransformerEngine) · observed Sep 20, 2026
- Last push (NVIDIA/TransformerEngine) · observed Sep 5, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: litgpt 14k · TransformerEngine 3.5k (synced Sep 20, 2026).
Common questions
- What is the difference between litgpt and TransformerEngine?
- litgpt: High-performance LLMs for pretraining, finetuning, and deployment. TransformerEngine: A library for accelerating Transformer models on NVIDIA GPUs using low precision formats like FP8 and FP4.. See the comparison table for live GitHub stats and shared categories.
- When should I choose litgpt over TransformerEngine?
- Choose litgpt over TransformerEngine when Tags unique to litgpt: ai, artificial-intelligence, large-language-models, llm; Also covers LLM Frameworks; When you need a comprehensive set of over 20 high-performance LLMs for pretraining, finetuning, and deployment.
- When should I choose TransformerEngine over litgpt?
- Choose TransformerEngine over litgpt when Tags unique to TransformerEngine: cuda, fp4, fp8, gpu; If you need high-throughput training or inference of Transformer models specifically on compatible NVIDIA GPUs (Hopper, Ada, Blackwell).
- When should I avoid litgpt?
- If you are looking for a tool that supports languages other than Python, as litgpt is exclusively Python-based. When you need a tool that offers fewer model options, as litgpt provides over 20 models which might be overwhelming for specific use cases. If you require proprietary licensing, as litgpt is open-source under the Apache-2.0 license.
- When should I avoid TransformerEngine?
- Avoid if your project is not running on NVIDIA's Hopper, Ada, or Blackwell GPUs. If memory usage isn't a critical concern and you prefer higher precision over speed optimization.
- Is litgpt or TransformerEngine more popular on GitHub?
- litgpt has more GitHub stars (13,667 vs 3,524). Stars measure visibility, not whether either tool fits your constraints.
- Are litgpt and TransformerEngine open source?
- Yes - both are open-source projects on GitHub (litgpt: Apache-2.0, TransformerEngine: Apache-2.0).
- Where can I find alternatives to litgpt or TransformerEngine?
- GraphCanon lists graph-backed alternatives at litgpt alternatives and TransformerEngine alternatives (litgpt markdown twin, TransformerEngine 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, litgpt or TransformerEngine?
- litgpt: Very active. TransformerEngine: Very active. 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 litgpt and TransformerEngine?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: litgpt trust report; TransformerEngine trust report.