--- title: "REST vs infinity" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/fasterdecoding-rest-vs-michaelfeil-infinity" tools: ["fasterdecoding-rest", "michaelfeil-infinity"] --- # REST vs infinity *GraphCanon updated Sep 20, 2026* ## Verdict Pick REST if rEST is a retrieval-based speculative decoding method for enhancing the efficiency of large language model inference, implemented in C under the Apache-2.0 license; pick infinity if infinity is a high-throughput, low-latency serving engine for text-embeddings, reranking models, CLIP, CLAP, and COLPAli, offering support for various hardware accelerators via CLI and Docker containers. [REST](https://github.com/FasterDecoding/REST) reports 220 GitHub stars, 17 forks, and 15 open issues, last pushed Mar 5, 2026. [infinity](https://michaelfeil.github.io/infinity/) has 2.9k stars, 207 forks, and 136 open issues, last pushed Mar 24, 2026. Figures are from public GitHub metadata via [REST's repository](https://github.com/FasterDecoding/REST) and [infinity's repository](https://github.com/michaelfeil/infinity). | | [REST](/tools/fasterdecoding-rest.md) | [infinity](/tools/michaelfeil-infinity.md) | | --- | --- | --- | | Tagline | REST: Retrieval-Based Speculative Decoding | High-throughput, low-latency serving engine for text-embeddings, reranking models, CLIP, CLAP, and COLPAli | | Stars | 220 | 2,942 | | Forks | 17 | 207 | | Open issues | 15 | 136 | | Language | C | Python | | Adopt for | REST is a retrieval-based speculative decoding method for enhancing the efficiency of large language model inference, implemented in C under the Apache-2.0 license. | Infinity is a high-throughput, low-latency serving engine for text-embeddings, reranking models, CLIP, CLAP, and COLPAli, offering support for various hardware accelerators via CLI and Docker containers. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [REST](/tools/fasterdecoding-rest.md) | [infinity](/tools/michaelfeil-infinity.md) | | --- | --- | --- | | Days since push | 196d | 178d | | Open issues (now) | 15 | 136 | | Stars delta | 0 (30d) | +35 (30d) | | Open issues delta | 0 (30d) | +6 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/fasterdecoding-rest/trust.md) | [trust report](/tools/michaelfeil-infinity/trust.md) | ## Shared compatibility - **Python**: [REST](/tools/fasterdecoding-rest.md) - Python runtime; [infinity](/tools/michaelfeil-infinity.md) - Python runtime ## Decision facts: REST - **Adopt for:** REST is a retrieval-based speculative decoding method for enhancing the efficiency of large language model inference, implemented in C under the Apache-2.0 license. ## Decision facts: infinity - **Adopt for:** Infinity is a high-throughput, low-latency serving engine for text-embeddings, reranking models, CLIP, CLAP, and COLPAli, offering support for various hardware accelerators via CLI and Docker containers. ## Choose when ### Choose REST if… - REST is primarily C; infinity is Python. - License: REST is Apache-2.0, infinity is MIT. - Tags unique to REST: llm-inference, retrieval, speculative-decoding. - Use REST when you need to improve the efficiency of large language model inference through retrieval-based speculative decoding. ### Choose infinity if… - infinity is primarily Python; REST is C. - License: infinity is MIT, REST is Apache-2.0. - Tags unique to infinity: bert-embeddings, cpu, docker, gpu. - Use Infinity when you need to serve text-embeddings and reranking models with high throughput and low latency, especially when you have access to hardware accelerators like GPUs or ROCm. ## When NOT to use REST - Avoid REST if your project does not require improvements in the efficiency of large language model inference. - Do not use REST if your project is not compatible with the C language or if you prefer a different license than Apache-2.0. ## When NOT to use infinity - Avoid using Infinity if you do not have access to hardware accelerators and prefer a lightweight solution, as the specialized Docker images for ROCm and ONNX-GPU are not built via CI/CD and may not be - Do not use Infinity if you need a serving engine that is frequently updated via CI/CD, as some specialized Docker images are not automatically built and may require manual version pinning. ## Common questions ### What is the difference between REST and infinity? REST: REST: Retrieval-Based Speculative Decoding. infinity: High-throughput, low-latency serving engine for text-embeddings, reranking models, CLIP, CLAP, and COLPAli. See the comparison table for live GitHub stats and shared categories. ### When should I choose REST over infinity? Choose REST over infinity when REST is primarily C; infinity is Python; License: REST is Apache-2.0, infinity is MIT; Tags unique to REST: llm-inference, retrieval, speculative-decoding; Use REST when you need to improve the efficiency of large language model inference through retrieval-based speculative decoding. ### When should I choose infinity over REST? Choose infinity over REST when infinity is primarily Python; REST is C; License: infinity is MIT, REST is Apache-2.0; Tags unique to infinity: bert-embeddings, cpu, docker, gpu; Use Infinity when you need to serve text-embeddings and reranking models with high throughput and low latency, especially when you have access to hardware accelerators like GPUs or ROCm. ### When should I avoid REST? Avoid REST if your project does not require improvements in the efficiency of large language model inference. Do not use REST if your project is not compatible with the C language or if you prefer a different license than Apache-2.0. ### When should I avoid infinity? Avoid using Infinity if you do not have access to hardware accelerators and prefer a lightweight solution, as the specialized Docker images for ROCm and ONNX-GPU are not built via CI/CD and may not be Do not use Infinity if you need a serving engine that is frequently updated via CI/CD, as some specialized Docker images are not automatically built and may require manual version pinning. ### Is REST or infinity more popular on GitHub? infinity has more GitHub stars (2,942 vs 220). Stars measure visibility, not whether either tool fits your constraints. ### Are REST and infinity open source? Yes - both are open-source projects on GitHub (REST: Apache-2.0, infinity: MIT). ### Where can I find alternatives to REST or infinity? GraphCanon lists graph-backed alternatives at [REST alternatives](/tools/fasterdecoding-rest/alternatives) and [infinity alternatives](/tools/michaelfeil-infinity/alternatives) ([REST markdown twin](/tools/fasterdecoding-rest/alternatives.md), [infinity markdown twin](/tools/michaelfeil-infinity/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-michaelfeil-infinity.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, REST or infinity? REST: Slowing. infinity: 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 REST and infinity? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [REST trust report](/tools/fasterdecoding-rest/trust); [infinity trust report](/tools/michaelfeil-infinity/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/_