Comparison
distributed-llama vs budgetml
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 budgetml if budgetML is a Python library that leverages Google Cloud Preemptible instances to decrease the cost of deploying machine learning models for inference.
Markdown twin · distributed-llama alternatives · budgetml alternatives
GraphCanon updated 1d
vs
Trust & integrity
| Signal | distributed-llama | budgetml |
|---|---|---|
| Maintenance | Steady (50d since push) As of 1d · github_public_v1 | Dormant (901d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 1d · github_public_v1 | Not a fork · Organization account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No published findings from this source as of 2026-07-11 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
- distributed-llama
- Distributed LLM inference using home devices cluster
- budgetml
- Deploys ML inference service economically
Stars
- distributed-llama
- 3.0k
- budgetml
- 1.3k
Forks
- distributed-llama
- 246
- budgetml
- 65
Open issues
- distributed-llama
- 48
- budgetml
- 4
Language
- distributed-llama
- C++
- budgetml
- 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.
- budgetml
- BudgetML is a Python library that leverages Google Cloud Preemptible instances to decrease the cost of deploying machine learning models for inference.
Persona
- distributed-llama
- -
- budgetml
- -
Runtime
- distributed-llama
- -
- budgetml
- -
License
- distributed-llama
- MIT
- budgetml
- The tool is distributed under the Apache-2.0 license, allowing for free use in both open source and commercial projects.
Last pushed
- distributed-llama
- Jul 5, 2026
- budgetml
- Feb 12, 2024
Categories
- distributed-llama
- Inference & Serving
- budgetml
- Inference & Serving
Trust and health
Maintenance
- distributed-llama
- Steady (60%)
- budgetml
- Dormant (18%)
Days since push
- distributed-llama
- 50d
- budgetml
- 901d
Open issues (now)
- distributed-llama
- 48
- budgetml
- 4
Stars delta
- distributed-llama
- +32 (30d)
- budgetml
- Unknown
Open issues delta
- distributed-llama
- 0 (30d)
- budgetml
- Unknown
Owner type
- distributed-llama
- User
- budgetml
- Organization
OSV dependency advisories
- distributed-llama
- No lockfile (source not queried)
- budgetml
- No published findings from this source as of 2026-07-11
Full report
- distributed-llama
- Trust report
- budgetml
- Trust report
Choose distributed-llama if…
- distributed-llama is primarily C++; budgetml is Python.
- License: distributed-llama is MIT, budgetml is Apache-2.0.
- Tags unique to distributed-llama: distributed-computing, llm-inference, 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 budgetml if…
- budgetml is primarily Python; distributed-llama is C++.
- License: budgetml is Apache-2.0, distributed-llama is MIT.
- Pricing: Free to use, but users will incur costs based on their usage of Google Cloud Preemptible instances..
- Requirements: Requires a working Python environment and access to Google Cloud services to deploy on Preemptible VMs; The library is available via PyPI or can be installed directly from GitHub for the latest features, at users' own risk.
- Tags unique to budgetml: api, data-science, deployment, fastapi.
- When you are looking to reduce costs significantly and have flexibility in your deployment schedule, since Preemptible instances can be interrupted
When NOT to use budgetml
- If you need absolute certainty that your ML service will not be interrupted at any point during operation
- Not suitable for continuous and uninterrupted services, as Google Cloud Preemptible instances can be terminated with short notice
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 Aug 24, 2026
- GitHub forks (b4rtaz/distributed-llama) · observed Aug 24, 2026
- Last push (b4rtaz/distributed-llama) · observed Jul 5, 2026
- License file (MIT) · observed Aug 24, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (ebhy/budgetml) · observed Aug 2, 2026
- GitHub forks (ebhy/budgetml) · observed Aug 2, 2026
- Last push (ebhy/budgetml) · observed Feb 12, 2024
- License file (Apache-2.0) · observed Aug 2, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: distributed-llama 3.0k · budgetml 1.3k (synced Aug 24, 2026).
Common questions
- What is the difference between distributed-llama and budgetml?
- distributed-llama: Distributed LLM inference using home devices cluster. budgetml: Deploys ML inference service economically. See the comparison table for live GitHub stats and shared categories.
- When should I choose distributed-llama over budgetml?
- Choose distributed-llama over budgetml when distributed-llama is primarily C++; budgetml is Python; License: distributed-llama is MIT, budgetml is Apache-2.0; Tags unique to distributed-llama: distributed-computing, llm-inference, 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 budgetml over distributed-llama?
- Choose budgetml over distributed-llama when budgetml is primarily Python; distributed-llama is C++; License: budgetml is Apache-2.0, distributed-llama is MIT; Pricing: Free to use, but users will incur costs based on their usage of Google Cloud Preemptible instances.; Requirements: Requires a working Python environment and access to Google Cloud services to deploy on Preemptible VMs; The library is available via PyPI or can be installed directly from GitHub for the latest features, at users' own risk; Tags unique to budgetml: api, data-science, deployment, fastapi; When you are looking to reduce costs significantly and have flexibility in your deployment schedule, since Preemptible instances can be interrupted.
- 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 budgetml?
- If you need absolute certainty that your ML service will not be interrupted at any point during operation Not suitable for continuous and uninterrupted services, as Google Cloud Preemptible instances can be terminated with short notice
- Is distributed-llama or budgetml more popular on GitHub?
- distributed-llama has more GitHub stars (3,044 vs 1,343). Stars measure visibility, not whether either tool fits your constraints.
- Are distributed-llama and budgetml open source?
- Yes - both are open-source projects on GitHub (distributed-llama: MIT, budgetml: Apache-2.0).
- Where can I find alternatives to distributed-llama or budgetml?
- GraphCanon lists graph-backed alternatives at distributed-llama alternatives and budgetml alternatives (distributed-llama markdown twin, budgetml 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 budgetml?
- distributed-llama: Steady. budgetml: 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 distributed-llama and budgetml?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: distributed-llama trust report; budgetml trust report.