Comparison
distributed-llama vs krasis
Verdict
Pick distributed-llama if distributed-llama is a C++ framework that leverages multiple home devices for faster large language model inference, under the MIT license; 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.
Markdown twin · distributed-llama alternatives · krasis alternatives
GraphCanon updated Sep 20, 2026
5views this month
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Trust & integrity
| Signal | distributed-llama | krasis |
|---|---|---|
| Maintenance | Steady (76d since push) As of Sep 19, 2026 · github_public_v1 | Very active (4d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 19, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 20, 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 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
- distributed-llama
- Distributed LLM inference using home devices cluster
- krasis
- Hybrid LLM Runtime for Efficient Large Model Inference on Consumer Hardware
Stars
- distributed-llama
- 3.1k
- krasis
- 521
Forks
- distributed-llama
- 250
- krasis
- 33
Open issues
- distributed-llama
- 48
- krasis
- 19
Language
- distributed-llama
- C++
- krasis
- C++
Adopt for
- distributed-llama
- distributed-llama is a C++ framework that leverages multiple home devices for faster large language model inference, under the MIT license.
- krasis
- Krasis is designed to offer efficient large model inference on consumer-grade hardware through hybrid CPU-GPU execution and high-performance optimization.
Persona
- distributed-llama
- -
- krasis
- -
Runtime
- distributed-llama
- -
- krasis
- -
License
- distributed-llama
- MIT
- krasis
- Other
Last pushed
- distributed-llama
- Jul 5, 2026
- krasis
- Sep 15, 2026
Categories
- distributed-llama
- Inference & Serving
- krasis
- Inference & Serving
Trust and health
Maintenance
- distributed-llama
- Steady (60%)
- krasis
- Very active (96%)
Days since push
- distributed-llama
- 76d
- krasis
- 4d
Open issues (now)
- distributed-llama
- 48
- krasis
- 19
Stars delta
- distributed-llama
- +48 (30d)
- krasis
- +37 (30d)
Open issues delta
- distributed-llama
- 0 (30d)
- krasis
- +11 (30d)
Full report
- distributed-llama
- Trust report
- krasis
- Trust report
Choose distributed-llama if…
- License: distributed-llama is MIT, krasis is Other.
- Tags unique to distributed-llama: distributed-computing, neural-network.
- When you have multiple interconnected home devices and want to maximize their combined computing power for LLM inference tasks.
When NOT to use distributed-llama
- For scenarios with fewer than two available devices, as the framework's capability to distribute and boost performance would be limited.
- In professional environments that require strict data privacy controls, due to potential network vulnerabilities among home devices.
Choose krasis if…
- License: krasis is Other, distributed-llama 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (b4rtaz/distributed-llama) · observed Sep 20, 2026
- GitHub forks (b4rtaz/distributed-llama) · observed Sep 20, 2026
- Last push (b4rtaz/distributed-llama) · observed Jul 5, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: distributed-llama 3.1k · krasis 521 (synced Sep 20, 2026).
Common questions
- What is the difference between distributed-llama and krasis?
- distributed-llama: Distributed LLM inference using home devices cluster. krasis: Hybrid LLM Runtime for Efficient Large Model Inference on Consumer Hardware. See the comparison table for live GitHub stats and shared categories.
- When should I choose distributed-llama over krasis?
- Choose distributed-llama over krasis when License: distributed-llama is MIT, krasis is Other; Tags unique to distributed-llama: distributed-computing, neural-network; When you have multiple interconnected home devices and want to maximize their combined computing power for LLM inference tasks.
- When should I choose krasis over distributed-llama?
- Choose krasis over distributed-llama when License: krasis is Other, distributed-llama 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 avoid distributed-llama?
- For scenarios with fewer than two available devices, as the framework's capability to distribute and boost performance would be limited. In professional environments that require strict data privacy controls, due to potential network vulnerabilities among home devices.
- 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.
- Is distributed-llama or krasis more popular on GitHub?
- distributed-llama has more GitHub stars (3,060 vs 521). Stars measure visibility, not whether either tool fits your constraints.
- Are distributed-llama and krasis open source?
- Yes - both are open-source projects on GitHub (distributed-llama: MIT, krasis: Other).
- Where can I find alternatives to distributed-llama or krasis?
- GraphCanon lists graph-backed alternatives at distributed-llama alternatives and krasis alternatives (distributed-llama markdown twin, krasis 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, distributed-llama or krasis?
- distributed-llama: Steady. krasis: 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 distributed-llama and krasis?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: distributed-llama trust report; krasis trust report.