Comparison
krasis vs airllm
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 airllm if airLLM is a notable framework designed specifically for running large language models on low-resource hardware, such as a single 4GB GPU.
Markdown twin · krasis alternatives · airllm alternatives
GraphCanon updated 3w
Trust & integrity
| Signal | krasis | airllm |
|---|---|---|
| Maintenance | Very active (0d since push) As of 4w · github_public_v1 | Very active (5d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 4w · github_public_v1 | Not a fork · Personal account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings 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
- airllm
- AirLLM 70B inference with single 4GB GPU
Stars
- krasis
- 484
- airllm
- 24k
Forks
- krasis
- 27
- airllm
- 2.7k
Open issues
- krasis
- 8
- airllm
- 115
Language
- krasis
- C++
- airllm
- Jupyter Notebook
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.
- airllm
- AirLLM is a notable framework designed specifically for running large language models on low-resource hardware, such as a single 4GB GPU.
Persona
- krasis
- -
- airllm
- -
Runtime
- krasis
- -
- airllm
- -
License
- krasis
- Other
- airllm
- Apache-2.0
Last pushed
- krasis
- Jul 25, 2026
- airllm
- Jul 23, 2026
Categories
- krasis
- Inference & Serving
- airllm
- Inference & Serving
Trust and health
Days since push
- krasis
- 0d
- airllm
- 5d
Open issues (now)
- krasis
- 8
- airllm
- 115
OSV dependency advisories
- krasis
- No lockfile (source not queried)
- airllm
- Published findings
Full report
- krasis
- Trust report
- airllm
- Trust report
Shared compatibility
- Python · krasis: Python runtime · airllm: Python runtime
Choose krasis if…
- krasis is primarily C++; airllm is Jupyter Notebook.
- License: krasis is Other, airllm 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 airllm if…
- airllm is primarily Jupyter Notebook; krasis is C++.
- License: airllm is Apache-2.0, krasis is Other.
- Pricing: Free and open-source under the Apache-2.0 license; however, infrastructure costs apply..
- Requirements: Min 16 GB RAM; A single 4GB GPU is sufficient for using this framework to run large language model inferences..
- Tags unique to airllm: chinese-llm, chinese-nlp, finetune, generative-ai.
- If you have limited hardware resources but need to perform inferences on large language models (like the 70B parameter model that AirLLM supports), use AirLLM.
When NOT to use airllm
- Avoid using AirLLM if you require models to run on higher-end GPUs or multiple GPU clusters, as its strength lies in low-resource efficiency.
- Do not use AirLLM if you are working primarily with non-Chinese language datasets and models, since support for other languages may be less optimized compared to competition.
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 (lyogavin/airllm) · observed Jul 28, 2026
- GitHub forks (lyogavin/airllm) · observed Jul 28, 2026
- Last push (lyogavin/airllm) · observed Jul 23, 2026
- License file (Apache-2.0) · observed Jul 28, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 9, 2026
GitHub stars on cards: krasis 484 · airllm 24k (synced Jul 25, 2026).
Common questions
- What is the difference between krasis and airllm?
- krasis: Hybrid LLM Runtime for Efficient Large Model Inference on Consumer Hardware. airllm: AirLLM 70B inference with single 4GB GPU. See the comparison table for live GitHub stats and shared categories.
- When should I choose krasis over airllm?
- Choose krasis over airllm when krasis is primarily C++; airllm is Jupyter Notebook; License: krasis is Other, airllm 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 airllm over krasis?
- Choose airllm over krasis when airllm is primarily Jupyter Notebook; krasis is C++; License: airllm is Apache-2.0, krasis is Other; Pricing: Free and open-source under the Apache-2.0 license; however, infrastructure costs apply.; Requirements: Min 16 GB RAM; A single 4GB GPU is sufficient for using this framework to run large language model inferences.; Tags unique to airllm: chinese-llm, chinese-nlp, finetune, generative-ai; If you have limited hardware resources but need to perform inferences on large language models (like the 70B parameter model that AirLLM supports), use AirLLM.
- 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 airllm?
- Avoid using AirLLM if you require models to run on higher-end GPUs or multiple GPU clusters, as its strength lies in low-resource efficiency. Do not use AirLLM if you are working primarily with non-Chinese language datasets and models, since support for other languages may be less optimized compared to competition.
- Is krasis or airllm more popular on GitHub?
- airllm has more GitHub stars (24,183 vs 484). Stars measure visibility, not whether either tool fits your constraints.
- Are krasis and airllm open source?
- Yes - both are open-source projects on GitHub (krasis: Other, airllm: Apache-2.0).
- Where can I find alternatives to krasis or airllm?
- GraphCanon lists graph-backed alternatives at krasis alternatives and airllm alternatives (krasis markdown twin, airllm 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 airllm?
- krasis: Very active. airllm: 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 airllm?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: krasis trust report; airllm trust report.