Comparison
REST vs airllm
Verdict
Pick REST if rEST is a retrieval-based speculative decoding tool implemented in C, designed for use cases that demand efficiency and fine-grained control over inference processes through its distinctive approach; pick airllm if airLLM is a notable framework designed specifically for running large language models on low-resource hardware, such as a single 4GB GPU.
Markdown twin · REST alternatives · airllm alternatives
GraphCanon updated 3w
Trust & integrity
| Signal | REST | airllm |
|---|---|---|
| Maintenance | Slowing (148d since push) As of 3w · github_public_v1 | Very active (5d since push) As of 4w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Personal account As of 4w · github_public_v1 |
| OSV dependency advisories | Published findings 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
- REST
- REST: Retrieval-Based Speculative Decoding
- airllm
- AirLLM 70B inference with single 4GB GPU
Stars
- REST
- 220
- airllm
- 24k
Forks
- REST
- 17
- airllm
- 2.7k
Open issues
- REST
- 15
- airllm
- 115
Language
- REST
- C
- airllm
- Jupyter Notebook
Adopt for
- REST
- REST is a retrieval-based speculative decoding tool implemented in C, designed for use cases that demand efficiency and fine-grained control over inference processes through its distinctive approach.
- airllm
- AirLLM is a notable framework designed specifically for running large language models on low-resource hardware, such as a single 4GB GPU.
Persona
- REST
- -
- airllm
- -
Runtime
- REST
- -
- airllm
- -
License
- REST
- Apache-2.0
- airllm
- Apache-2.0
Last pushed
- REST
- Mar 5, 2026
- airllm
- Jul 23, 2026
Categories
- REST
- Data & Retrieval, Inference & Serving
- airllm
- Inference & Serving
Trust and health
Maintenance
- REST
- Slowing (36%)
- airllm
- Very active (96%)
Days since push
- REST
- 148d
- airllm
- 5d
Open issues (now)
- REST
- 15
- airllm
- 115
Owner type
- REST
- Organization
- airllm
- User
Full report
- REST
- Trust report
- airllm
- Trust report
Shared compatibility
- Python · REST: Python runtime · airllm: Python runtime
Choose REST if…
- REST is primarily C; airllm is Jupyter Notebook.
- Tags unique to REST: llm-inference, retrieval, speculative-decoding.
- Also covers Data & Retrieval.
- - When you need high performance and are willing to work with the C language for customization and optimization.
When NOT to use REST
- - Avoid if your team lacks proficiency in C programming as this may lead to an overhead in developing and maintaining the tool.
- - Not recommended for projects where flexibility with commonly used high-level languages like Python is essential, as REST primarily relies on lower-level language capabilities.
Choose airllm if…
- airllm is primarily Jupyter Notebook; REST is C.
- Pricing: Free and open-source under the Apache-2.0 license; however, infrastructure costs apply..
- Requirements: Min 16 GB RAM; A single 4GB GPU is sufficient for using this framework to run large language model inferences..
- Tags unique to airllm: chinese-llm, chinese-nlp, finetune, generative-ai.
- If you have limited hardware resources but need to perform inferences on large language models (like the 70B parameter model that AirLLM supports), use AirLLM.
When NOT to use airllm
- Avoid using AirLLM if you require models to run on higher-end GPUs or multiple GPU clusters, as its strength lies in low-resource efficiency.
- Do not use AirLLM if you are working primarily with non-Chinese language datasets and models, since support for other languages may be less optimized compared to competition.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (FasterDecoding/REST) · observed Aug 1, 2026
- GitHub forks (FasterDecoding/REST) · observed Aug 1, 2026
- Last push (FasterDecoding/REST) · observed Mar 5, 2026
- License file (Apache-2.0) · observed Aug 1, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (lyogavin/airllm) · observed Jul 28, 2026
- GitHub forks (lyogavin/airllm) · observed Jul 28, 2026
- Last push (lyogavin/airllm) · observed Jul 23, 2026
- License file (Apache-2.0) · observed Jul 28, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 9, 2026
GitHub stars on cards: REST 220 · airllm 24k (synced Aug 1, 2026).
Common questions
- What is the difference between REST and airllm?
- REST: REST: Retrieval-Based Speculative Decoding. airllm: AirLLM 70B inference with single 4GB GPU. See the comparison table for live GitHub stats and shared categories.
- When should I choose REST over airllm?
- Choose REST over airllm when REST is primarily C; airllm is Jupyter Notebook; Tags unique to REST: llm-inference, retrieval, speculative-decoding; Also covers Data & Retrieval; - When you need high performance and are willing to work with the C language for customization and optimization.
- When should I choose airllm over REST?
- Choose airllm over REST when airllm is primarily Jupyter Notebook; REST is C; Pricing: Free and open-source under the Apache-2.0 license; however, infrastructure costs apply.; Requirements: Min 16 GB RAM; A single 4GB GPU is sufficient for using this framework to run large language model inferences.; Tags unique to airllm: chinese-llm, chinese-nlp, finetune, generative-ai; If you have limited hardware resources but need to perform inferences on large language models (like the 70B parameter model that AirLLM supports), use AirLLM.
- When should I avoid REST?
- - Avoid if your team lacks proficiency in C programming as this may lead to an overhead in developing and maintaining the tool. - Not recommended for projects where flexibility with commonly used high-level languages like Python is essential, as REST primarily relies on lower-level language capabilities.
- When should I avoid airllm?
- Avoid using AirLLM if you require models to run on higher-end GPUs or multiple GPU clusters, as its strength lies in low-resource efficiency. Do not use AirLLM if you are working primarily with non-Chinese language datasets and models, since support for other languages may be less optimized compared to competition.
- Is REST or airllm more popular on GitHub?
- airllm has more GitHub stars (24,183 vs 220). Stars measure visibility, not whether either tool fits your constraints.
- Are REST and airllm open source?
- Yes - both are open-source projects on GitHub (REST: Apache-2.0, airllm: Apache-2.0).
- Where can I find alternatives to REST or airllm?
- GraphCanon lists graph-backed alternatives at REST alternatives and airllm alternatives (REST markdown twin, airllm 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, REST or airllm?
- REST: Slowing. airllm: 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 REST and airllm?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: REST trust report; airllm trust report.