--- title: "REST vs airllm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/fasterdecoding-rest-vs-lyogavin-airllm" tools: ["fasterdecoding-rest", "lyogavin-airllm"] --- # REST vs airllm *GraphCanon updated Jul 12, 2026* ## 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. [REST](https://github.com/FasterDecoding/REST) reports 220 GitHub stars, 17 forks, and 15 open issues, last pushed Mar 5, 2026. [airllm](https://github.com/lyogavin/airllm) has 22k stars, 2.6k forks, and 106 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [REST's repository](https://github.com/FasterDecoding/REST) and [airllm's repository](https://github.com/lyogavin/airllm). | | [REST](/tools/fasterdecoding-rest.md) | [airllm](/tools/lyogavin-airllm.md) | | --- | --- | --- | | Tagline | REST: Retrieval-Based Speculative Decoding | AirLLM 70B inference with single 4GB GPU | | Stars | 220 | 22,399 | | Forks | 17 | 2,581 | | Open issues | 15 | 106 | | Language | C | Jupyter Notebook | | Adopt for | 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 is a notable framework designed specifically for running large language models on low-resource hardware, such as a single 4GB GPU. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Data & Retrieval, Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [REST](/tools/fasterdecoding-rest.md) | [airllm](/tools/lyogavin-airllm.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 128d | 0d | | Open issues (now) | 15 | 106 | | Owner type | Organization | User | | Security scan | 2 low (2 low) | 4 low (4 low) | | Full report | [trust report](/tools/fasterdecoding-rest/trust.md) | [trust report](/tools/lyogavin-airllm/trust.md) | ## Decision facts: REST - **Adopt for:** 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. ## Decision facts: airllm - **Pricing:** freemium - 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. - **Adopt for:** AirLLM is a notable framework designed specifically for running large language models on low-resource hardware, such as a single 4GB GPU. - **License detail:** Apache-2.0 ## Choose when ### 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. ### 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 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 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. ## 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 (22,399 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](/tools/fasterdecoding-rest/alternatives) and [airllm alternatives](/tools/lyogavin-airllm/alternatives) ([REST markdown twin](/tools/fasterdecoding-rest/alternatives.md), [airllm markdown twin](/tools/lyogavin-airllm/alternatives.md)), 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](/compare/fasterdecoding-rest-vs-lyogavin-airllm.md) 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](/tools/fasterdecoding-rest/trust); [airllm trust report](/tools/lyogavin-airllm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=fasterdecoding-rest`](/api/graphcanon/graph?tool=fasterdecoding-rest) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_