Comparison
krasis vs Awesome-LLM-Inference
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 Awesome-LLM-Inference if awesome-LLM-Inference is a well-curated list of papers and codes related to efficient inference techniques for large language models and vision-language models, featuring methods like Flash-Attention and Paged-Attention.
Markdown twin · krasis alternatives · Awesome-LLM-Inference alternatives
GraphCanon updated 4w
vs
Trust & integrity
| Signal | krasis | Awesome-LLM-Inference |
|---|---|---|
| Maintenance | Very active (0d since push) As of 4w · github_public_v1 | Steady (32d since push) As of 4w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 4w · github_public_v1 | Not a fork · Organization account As of 4w · 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
- krasis
- Hybrid LLM Runtime for Efficient Large Model Inference on Consumer Hardware
- Awesome-LLM-Inference
- A curated list of LLM/VLM inference papers with codes
Stars
- krasis
- 484
- Awesome-LLM-Inference
- 5.4k
Forks
- krasis
- 27
- Awesome-LLM-Inference
- 428
Open issues
- krasis
- 8
- Awesome-LLM-Inference
- 6
Language
- krasis
- C++
- Awesome-LLM-Inference
- 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.
- Awesome-LLM-Inference
- Awesome-LLM-Inference is a well-curated list of papers and codes related to efficient inference techniques for large language models and vision-language models, featuring methods like Flash-Attention and Paged-Attention.
Persona
- krasis
- -
- Awesome-LLM-Inference
- -
Runtime
- krasis
- -
- Awesome-LLM-Inference
- -
License
- krasis
- Other
- Awesome-LLM-Inference
- The tool is licensed under GPL-3.0, which may affect how it can be integrated into other projects depending on their licensing needs.
Last pushed
- krasis
- Jul 25, 2026
- Awesome-LLM-Inference
- Jun 23, 2026
Categories
- krasis
- Inference & Serving
- Awesome-LLM-Inference
- Inference & Serving
Trust and health
Maintenance
- krasis
- Very active (96%)
- Awesome-LLM-Inference
- Steady (60%)
Days since push
- krasis
- 0d
- Awesome-LLM-Inference
- 32d
Open issues (now)
- krasis
- 8
- Awesome-LLM-Inference
- 6
Owner type
- krasis
- User
- Awesome-LLM-Inference
- Organization
Full report
- krasis
- Trust report
- Awesome-LLM-Inference
- Trust report
Choose krasis if…
- krasis is primarily C++; Awesome-LLM-Inference is Python.
- License: krasis is Other, Awesome-LLM-Inference is GPL-3.0.
- 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 Awesome-LLM-Inference if…
- Awesome-LLM-Inference is primarily Python; krasis is C++.
- License: Awesome-LLM-Inference is GPL-3.0, krasis is Other.
- Requirements: Requires Python for the use of included codes and to understand the methods described in the associated papers..
- Tags unique to Awesome-LLM-Inference: flash-attention, paged-attention, parallelism, wint8/4.
- Use Awesome-LLM-Inference when you are looking to optimize the performance of your large language model or vision-language model inference with cutting-edge techniques such as Flash-Attention.
When NOT to use Awesome-LLM-Inference
- Do not use Awesome-LLM-Inference if your project strictly conforms to licenses different from GPL-3.0, as its licensing could be incompatible with your project's license requirements.
- Avoid using this tool for immediate production implementation of inference techniques without additional vetting since the repository itself may contain unvetted research papers and code snippets.
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 Jul 25, 2026
- GitHub forks (brontoguana/krasis) · observed Jul 25, 2026
- Last push (brontoguana/krasis) · observed Jul 25, 2026
- License file (Other) · observed Jul 25, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (xlite-dev/Awesome-LLM-Inference) · observed Jul 25, 2026
- GitHub forks (xlite-dev/Awesome-LLM-Inference) · observed Jul 25, 2026
- Last push (xlite-dev/Awesome-LLM-Inference) · observed Jun 23, 2026
- License file (GPL-3.0) · observed Jul 25, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: krasis 484 · Awesome-LLM-Inference 5.4k (synced Jul 25, 2026).
Common questions
- What is the difference between krasis and Awesome-LLM-Inference?
- krasis: Hybrid LLM Runtime for Efficient Large Model Inference on Consumer Hardware. Awesome-LLM-Inference: A curated list of LLM/VLM inference papers with codes. See the comparison table for live GitHub stats and shared categories.
- When should I choose krasis over Awesome-LLM-Inference?
- Choose krasis over Awesome-LLM-Inference when krasis is primarily C++; Awesome-LLM-Inference is Python; License: krasis is Other, Awesome-LLM-Inference is GPL-3.0; 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 Awesome-LLM-Inference over krasis?
- Choose Awesome-LLM-Inference over krasis when Awesome-LLM-Inference is primarily Python; krasis is C++; License: Awesome-LLM-Inference is GPL-3.0, krasis is Other; Requirements: Requires Python for the use of included codes and to understand the methods described in the associated papers.; Tags unique to Awesome-LLM-Inference: flash-attention, paged-attention, parallelism, wint8/4; Use Awesome-LLM-Inference when you are looking to optimize the performance of your large language model or vision-language model inference with cutting-edge techniques such as Flash-Attention.
- 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 Awesome-LLM-Inference?
- Do not use Awesome-LLM-Inference if your project strictly conforms to licenses different from GPL-3.0, as its licensing could be incompatible with your project's license requirements. Avoid using this tool for immediate production implementation of inference techniques without additional vetting since the repository itself may contain unvetted research papers and code snippets.
- Is krasis or Awesome-LLM-Inference more popular on GitHub?
- Awesome-LLM-Inference has more GitHub stars (5,415 vs 484). Stars measure visibility, not whether either tool fits your constraints.
- Are krasis and Awesome-LLM-Inference open source?
- Yes - both are open-source projects on GitHub (krasis: Other, Awesome-LLM-Inference: GPL-3.0).
- Where can I find alternatives to krasis or Awesome-LLM-Inference?
- GraphCanon lists graph-backed alternatives at krasis alternatives and Awesome-LLM-Inference alternatives (krasis markdown twin, Awesome-LLM-Inference 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 Awesome-LLM-Inference?
- krasis: Very active. Awesome-LLM-Inference: Steady. 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 Awesome-LLM-Inference?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: krasis trust report; Awesome-LLM-Inference trust report.