Comparison
distributed-llama vs sarathi-serve
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 sarathi-serve if sarathi Serve targets efficient low-latency and high-throughput inference for LLMs using Python.
Markdown twin · distributed-llama alternatives · sarathi-serve alternatives
GraphCanon updated Sep 20, 2026
6views this month
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Trust & integrity
| Signal | distributed-llama | sarathi-serve |
|---|---|---|
| Maintenance | Steady (76d since push) As of Sep 19, 2026 · github_public_v1 | Slowing (254d 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 · Organization 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
- sarathi-serve
- A low-latency and high-throughput serving engine for LLMs
Stars
- distributed-llama
- 3.1k
- sarathi-serve
- 527
Forks
- distributed-llama
- 250
- sarathi-serve
- 67
Open issues
- distributed-llama
- 48
- sarathi-serve
- 17
Language
- distributed-llama
- C++
- sarathi-serve
- 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.
- sarathi-serve
- Sarathi Serve targets efficient low-latency and high-throughput inference for LLMs using Python.
Persona
- distributed-llama
- -
- sarathi-serve
- -
Runtime
- distributed-llama
- -
- sarathi-serve
- -
License
- distributed-llama
- MIT
- sarathi-serve
- Apache-2.0
Last pushed
- distributed-llama
- Jul 5, 2026
- sarathi-serve
- Jan 8, 2026
Categories
- distributed-llama
- Inference & Serving
- sarathi-serve
- Inference & Serving
Trust and health
Maintenance
- distributed-llama
- Steady (60%)
- sarathi-serve
- Slowing (36%)
Days since push
- distributed-llama
- 76d
- sarathi-serve
- 254d
Open issues (now)
- distributed-llama
- 48
- sarathi-serve
- 17
Stars delta
- distributed-llama
- +48 (30d)
- sarathi-serve
- +15 (30d)
Open issues delta
- distributed-llama
- 0 (30d)
- sarathi-serve
- +1 (30d)
Owner type
- distributed-llama
- User
- sarathi-serve
- Organization
Full report
- distributed-llama
- Trust report
- sarathi-serve
- Trust report
Choose distributed-llama if…
- distributed-llama is primarily C++; sarathi-serve is Python.
- License: distributed-llama is MIT, sarathi-serve 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 sarathi-serve if…
- sarathi-serve is primarily Python; distributed-llama is C++.
- License: sarathi-serve is Apache-2.0, distributed-llama is MIT.
- Tags unique to sarathi-serve: llama, pytorch, transformer.
- Optimize Python-based projects needing quick responses from large language models.
When NOT to use sarathi-serve
- Necessitate a non-Python environment for deployment and operation.
- Prefer a tool that incorporates more than just low-latency, high-throughput focus such as multi-language support or specialized optimizations.
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 (microsoft/sarathi-serve) · observed Sep 20, 2026
- GitHub forks (microsoft/sarathi-serve) · observed Sep 20, 2026
- Last push (microsoft/sarathi-serve) · observed Jan 8, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: distributed-llama 3.1k · sarathi-serve 527 (synced Sep 20, 2026).
Common questions
- What is the difference between distributed-llama and sarathi-serve?
- distributed-llama: Distributed LLM inference using home devices cluster. sarathi-serve: A low-latency and high-throughput serving engine for LLMs. See the comparison table for live GitHub stats and shared categories.
- When should I choose distributed-llama over sarathi-serve?
- Choose distributed-llama over sarathi-serve when distributed-llama is primarily C++; sarathi-serve is Python; License: distributed-llama is MIT, sarathi-serve 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 sarathi-serve over distributed-llama?
- Choose sarathi-serve over distributed-llama when sarathi-serve is primarily Python; distributed-llama is C++; License: sarathi-serve is Apache-2.0, distributed-llama is MIT; Tags unique to sarathi-serve: llama, pytorch, transformer; Optimize Python-based projects needing quick responses from large language models.
- 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 sarathi-serve?
- Necessitate a non-Python environment for deployment and operation. Prefer a tool that incorporates more than just low-latency, high-throughput focus such as multi-language support or specialized optimizations.
- Is distributed-llama or sarathi-serve more popular on GitHub?
- distributed-llama has more GitHub stars (3,060 vs 527). Stars measure visibility, not whether either tool fits your constraints.
- Are distributed-llama and sarathi-serve open source?
- Yes - both are open-source projects on GitHub (distributed-llama: MIT, sarathi-serve: Apache-2.0).
- Where can I find alternatives to distributed-llama or sarathi-serve?
- GraphCanon lists graph-backed alternatives at distributed-llama alternatives and sarathi-serve alternatives (distributed-llama markdown twin, sarathi-serve 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 sarathi-serve?
- distributed-llama: Steady. sarathi-serve: Slowing. 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 sarathi-serve?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: distributed-llama trust report; sarathi-serve trust report.