Comparison
krasis vs exllama
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 exllama if exLlama provides a memory-efficient implementation of the LLaMa model with support for quantized weights, primarily aimed at users with NVIDIA GPUs from the 30-series onwards.
Markdown twin · krasis alternatives · exllama alternatives
GraphCanon updated 2w
Trust & integrity
| Signal | krasis | exllama |
|---|---|---|
| Maintenance | Very active (0d since push) As of 4w · github_public_v1 | Dormant (1041d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 4w · github_public_v1 | Not a fork · Personal account As of 2w · 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
- exllama
- Memory-efficient rewrite of HF transformers for Llama with quantized weights
Stars
- krasis
- 484
- exllama
- 2.9k
Forks
- krasis
- 27
- exllama
- 220
Open issues
- krasis
- 8
- exllama
- 65
Language
- krasis
- C++
- exllama
- 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.
- exllama
- ExLlama provides a memory-efficient implementation of the LLaMa model with support for quantized weights, primarily aimed at users with NVIDIA GPUs from the 30-series onwards.
Persona
- krasis
- -
- exllama
- -
Runtime
- krasis
- -
- exllama
- -
License
- krasis
- Other
- exllama
- MIT
Last pushed
- krasis
- Jul 25, 2026
- exllama
- Sep 30, 2023
Categories
- krasis
- Inference & Serving
- exllama
- Inference & Serving, LLM Frameworks
Trust and health
Maintenance
- krasis
- Very active (96%)
- exllama
- Dormant (18%)
Days since push
- krasis
- 0d
- exllama
- 1041d
Open issues (now)
- krasis
- 8
- exllama
- 65
OSV dependency advisories
- krasis
- No lockfile (source not queried)
- exllama
- Published findings
Full report
- krasis
- Trust report
- exllama
- Trust report
Choose krasis if…
- krasis is primarily C++; exllama is Python.
- License: krasis is Other, exllama is MIT.
- 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 exllama if…
- exllama is primarily Python; krasis is C++.
- License: exllama is MIT, krasis is Other.
- Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu.
- Also covers LLM Frameworks.
- exllama ships Docker support for self-hosted deployment.
- - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support.
When NOT to use exllama
- - If you are operating older GPUs such as Pascal series, which lack robust FP16 support; alternatives like AutoGPTQ might perform better.
- - In scenarios that involve AMD GPU hardware (due to limited testing and optimization efforts).
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 (turboderp/exllama) · observed Aug 7, 2026
- GitHub forks (turboderp/exllama) · observed Aug 7, 2026
- Last push (turboderp/exllama) · observed Sep 30, 2023
- License file (MIT) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: krasis 484 · exllama 2.9k (synced Jul 25, 2026).
Common questions
- What is the difference between krasis and exllama?
- krasis: Hybrid LLM Runtime for Efficient Large Model Inference on Consumer Hardware. exllama: Memory-efficient rewrite of HF transformers for Llama with quantized weights. See the comparison table for live GitHub stats and shared categories.
- When should I choose krasis over exllama?
- Choose krasis over exllama when krasis is primarily C++; exllama is Python; License: krasis is Other, exllama is MIT; 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 exllama over krasis?
- Choose exllama over krasis when exllama is primarily Python; krasis is C++; License: exllama is MIT, krasis is Other; Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu; Also covers LLM Frameworks; exllama ships Docker support for self-hosted deployment; - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support.
- 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 exllama?
- - If you are operating older GPUs such as Pascal series, which lack robust FP16 support; alternatives like AutoGPTQ might perform better. - In scenarios that involve AMD GPU hardware (due to limited testing and optimization efforts).
- Is krasis or exllama more popular on GitHub?
- exllama has more GitHub stars (2,937 vs 484). Stars measure visibility, not whether either tool fits your constraints.
- Are krasis and exllama open source?
- Yes - both are open-source projects on GitHub (krasis: Other, exllama: MIT).
- Where can I find alternatives to krasis or exllama?
- GraphCanon lists graph-backed alternatives at krasis alternatives and exllama alternatives (krasis markdown twin, exllama 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 exllama?
- krasis: Very active. exllama: 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 krasis and exllama?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: krasis trust report; exllama trust report.