Comparison
krasis vs litgpt
Verdict
Pick krasis if krasis is designed to offer efficient large model inference on consumer-grade hardware through hybrid CPU-GPU execution and high-performance optimization; 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.
Markdown twin · krasis alternatives · litgpt alternatives
GraphCanon updated Sep 20, 2026
6views this month
Trust & integrity
| Signal | krasis | litgpt |
|---|---|---|
| Maintenance | Very active (4d since push) As of Sep 20, 2026 · github_public_v1 | Very active (3d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Sep 18, 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
- krasis
- Hybrid LLM Runtime for Efficient Large Model Inference on Consumer Hardware
- litgpt
- High-performance LLMs for pretraining, finetuning, and deployment
Stars
- krasis
- 521
- litgpt
- 14k
Forks
- krasis
- 33
- litgpt
- 1.5k
Open issues
- krasis
- 19
- litgpt
- 290
Language
- krasis
- C++
- litgpt
- Python
Adopt for
- krasis
- Krasis is designed to offer efficient large model inference on consumer-grade hardware through hybrid CPU-GPU execution and high-performance optimization.
- 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.
Persona
- krasis
- -
- litgpt
- -
Runtime
- krasis
- -
- litgpt
- -
License
- krasis
- Other
- litgpt
- Apache-2.0
Last pushed
- krasis
- Sep 15, 2026
- litgpt
- Sep 14, 2026
Categories
- krasis
- Inference & Serving
- litgpt
- Inference & Serving, LLM Frameworks, Model Training
Trust and health
Days since push
- krasis
- 4d
- litgpt
- 3d
Open issues (now)
- krasis
- 19
- litgpt
- 290
Stars delta
- krasis
- +37 (30d)
- litgpt
- +62 (30d)
Open issues delta
- krasis
- +11 (30d)
- litgpt
- +18 (30d)
Owner type
- krasis
- User
- litgpt
- Organization
Full report
- krasis
- Trust report
- litgpt
- Trust report
Shared compatibility
- Python · krasis: Python runtime · litgpt: Python runtime
Choose krasis if…
- krasis is primarily C++; litgpt is Python.
- License: krasis is Other, litgpt is Apache-2.0.
- Tags unique to krasis: cpu-inference, gguf-model-support, gpu-inference, high-performance-inference.
- - When aiming for efficient operation of larger language models with limited VRAM, as Krasis optimizes memory utilization specifically to support this scenario.
When NOT to use krasis
- - Avoid using Krasis if your hardware setup does not include both CPU and GPU capabilities, as its hybrid execution relies on utilizing both components for optimal performance.
- - If you prioritize running lightweight models with minimal memory footprint on low-end devices, Krasis might not be the ideal choice given it is optimized for larger-scale model inference.
Choose litgpt if…
- litgpt is primarily Python; krasis is C++.
- License: litgpt is Apache-2.0, krasis is Other.
- Tags unique to litgpt: ai, artificial-intelligence, deep-learning, large-language-models.
- Also covers LLM Frameworks, Model Training.
- 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (brontoguana/krasis) · observed Sep 20, 2026
- GitHub forks (brontoguana/krasis) · observed Sep 20, 2026
- Last push (brontoguana/krasis) · observed Sep 15, 2026
- License file (Other) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: krasis 521 · litgpt 14k (synced Sep 20, 2026).
Common questions
- What is the difference between krasis and litgpt?
- krasis: Hybrid LLM Runtime for Efficient Large Model Inference on Consumer Hardware. litgpt: High-performance LLMs for pretraining, finetuning, and deployment. See the comparison table for live GitHub stats and shared categories.
- When should I choose krasis over litgpt?
- Choose krasis over litgpt when krasis is primarily C++; litgpt is Python; License: krasis is Other, litgpt is Apache-2.0; Tags unique to krasis: cpu-inference, gguf-model-support, gpu-inference, high-performance-inference; - When aiming for efficient operation of larger language models with limited VRAM, as Krasis optimizes memory utilization specifically to support this scenario.
- When should I choose litgpt over krasis?
- Choose litgpt over krasis when litgpt is primarily Python; krasis is C++; License: litgpt is Apache-2.0, krasis is Other; Tags unique to litgpt: ai, artificial-intelligence, deep-learning, large-language-models; Also covers LLM Frameworks, Model Training; When you need a comprehensive set of over 20 high-performance LLMs for pretraining, finetuning, and deployment.
- When should I avoid krasis?
- - Avoid using Krasis if your hardware setup does not include both CPU and GPU capabilities, as its hybrid execution relies on utilizing both components for optimal performance. - If you prioritize running lightweight models with minimal memory footprint on low-end devices, Krasis might not be the ideal choice given it is optimized for larger-scale model inference.
- 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.
- Is krasis or litgpt more popular on GitHub?
- litgpt has more GitHub stars (13,667 vs 521). Stars measure visibility, not whether either tool fits your constraints.
- Are krasis and litgpt open source?
- Yes - both are open-source projects on GitHub (krasis: Other, litgpt: Apache-2.0).
- Where can I find alternatives to krasis or litgpt?
- GraphCanon lists graph-backed alternatives at krasis alternatives and litgpt alternatives (krasis markdown twin, litgpt 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, krasis or litgpt?
- krasis: Very active. litgpt: 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 krasis and litgpt?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: krasis trust report; litgpt trust report.