Comparison
distributed-llama vs kvcached
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 kvcached if kvcached is designed for optimizing dynamic GPU sharing and multiplexing scenarios, beneficial for LLM inference and serving operations.
Markdown twin · distributed-llama alternatives · kvcached alternatives
GraphCanon updated Sep 20, 2026
8views this month
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Trust & integrity
| Signal | distributed-llama | kvcached |
|---|---|---|
| Maintenance | Steady (76d since push) As of Sep 19, 2026 · github_public_v1 | Very active (0d since push) As of Sep 19, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 19, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 19, 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
- kvcached
- Virtualized Elastic KV Cache for Dynamic GPU Sharing and Beyond
Stars
- distributed-llama
- 3.1k
- kvcached
- 1.4k
Forks
- distributed-llama
- 250
- kvcached
- 165
Open issues
- distributed-llama
- 48
- kvcached
- 114
Language
- distributed-llama
- C++
- kvcached
- Python
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.
- kvcached
- Kvcached is designed for optimizing dynamic GPU sharing and multiplexing scenarios, beneficial for LLM inference and serving operations.
Persona
- distributed-llama
- -
- kvcached
- -
Runtime
- distributed-llama
- -
- kvcached
- -
License
- distributed-llama
- MIT
- kvcached
- Apache-2.0
Last pushed
- distributed-llama
- Jul 5, 2026
- kvcached
- Sep 18, 2026
Categories
- distributed-llama
- Inference & Serving
- kvcached
- Inference & Serving, LLM Frameworks
Trust and health
Maintenance
- distributed-llama
- Steady (60%)
- kvcached
- Very active (96%)
Days since push
- distributed-llama
- 76d
- kvcached
- 0d
Open issues (now)
- distributed-llama
- 48
- kvcached
- 114
Stars delta
- distributed-llama
- +48 (30d)
- kvcached
- +309 (30d)
Open issues delta
- distributed-llama
- 0 (30d)
- kvcached
- +10 (30d)
Owner type
- distributed-llama
- User
- kvcached
- Organization
Full report
- distributed-llama
- Trust report
- kvcached
- Trust report
Choose distributed-llama if…
- distributed-llama is primarily C++; kvcached is Python.
- License: distributed-llama is MIT, kvcached is Apache-2.0.
- 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 kvcached if…
- kvcached is primarily Python; distributed-llama is C++.
- License: kvcached is Apache-2.0, distributed-llama is MIT.
- Tags unique to kvcached: elastic-kvcache, gpu-sharing, kvcache.
- Also covers LLM Frameworks.
- If you are looking to optimize performance in environments that require dynamic allocation of GPUs among multiple processes or tasks.
When NOT to use kvcached
- For applications where static, predefined resource allocations are sufficient and do not require dynamic adjustments to GPU usage.
- In scenarios that prioritize simplicity over sophisticated resource management, as Kvcached may add unnecessary complexity with its advanced features for dynamic GPU sharing.
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 (ovg-project/kvcached) · observed Sep 20, 2026
- GitHub forks (ovg-project/kvcached) · observed Sep 20, 2026
- Last push (ovg-project/kvcached) · observed Sep 18, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: distributed-llama 3.1k · kvcached 1.4k (synced Sep 20, 2026).
Common questions
- What is the difference between distributed-llama and kvcached?
- distributed-llama: Distributed LLM inference using home devices cluster. kvcached: Virtualized Elastic KV Cache for Dynamic GPU Sharing and Beyond. See the comparison table for live GitHub stats and shared categories.
- When should I choose distributed-llama over kvcached?
- Choose distributed-llama over kvcached when distributed-llama is primarily C++; kvcached is Python; License: distributed-llama is MIT, kvcached is Apache-2.0; 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 kvcached over distributed-llama?
- Choose kvcached over distributed-llama when kvcached is primarily Python; distributed-llama is C++; License: kvcached is Apache-2.0, distributed-llama is MIT; Tags unique to kvcached: elastic-kvcache, gpu-sharing, kvcache; Also covers LLM Frameworks; If you are looking to optimize performance in environments that require dynamic allocation of GPUs among multiple processes or tasks.
- 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 kvcached?
- For applications where static, predefined resource allocations are sufficient and do not require dynamic adjustments to GPU usage. In scenarios that prioritize simplicity over sophisticated resource management, as Kvcached may add unnecessary complexity with its advanced features for dynamic GPU sharing.
- Is distributed-llama or kvcached more popular on GitHub?
- distributed-llama has more GitHub stars (3,060 vs 1,424). Stars measure visibility, not whether either tool fits your constraints.
- Are distributed-llama and kvcached open source?
- Yes - both are open-source projects on GitHub (distributed-llama: MIT, kvcached: Apache-2.0).
- Where can I find alternatives to distributed-llama or kvcached?
- GraphCanon lists graph-backed alternatives at distributed-llama alternatives and kvcached alternatives (distributed-llama markdown twin, kvcached 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 kvcached?
- distributed-llama: Steady. kvcached: 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 kvcached?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: distributed-llama trust report; kvcached trust report.