--- title: "Yi vs llm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/01-ai-yi-vs-simonw-llm" tools: ["01-ai-yi", "simonw-llm"] --- # Yi vs llm *GraphCanon updated Sep 30, 2026* ## Verdict Pick Yi if yi is a series of large language models trained from scratch, offering local deployment and inference capabilities. It is suitable for users who need to run large language models locally and have the necessary hardware,如; pick llm if decision-critical facts for 'llm'. [Yi](https://01.ai) reports 7.8k GitHub stars, 499 forks, and 31 open issues, last pushed Nov 27, 2024. [llm](https://llm.datasette.io) has 12k stars, 978 forks, and 689 open issues, last pushed Sep 2, 2026. Figures are from public GitHub metadata via [Yi's repository](https://github.com/01-ai/Yi) and [llm's repository](https://github.com/simonw/llm). | | [Yi](/tools/01-ai-yi.md) | [llm](/tools/simonw-llm.md) | | --- | --- | --- | | Tagline | A series of large language models trained from scratch | Access large language models from the command-line | | Stars | 7,833 | 12,473 | | Forks | 499 | 978 | | Open issues | 31 | 689 | | Language | Jupyter Notebook | Python | | Adopt for | Yi is a series of large language models trained from scratch, offering local deployment and inference capabilities. It is suitable for users who need to run large language models locally and have the necessary hardware,如 | Decision-critical facts for 'llm' | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Inference & Serving, LLM Frameworks | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Yi](/tools/01-ai-yi.md) | [llm](/tools/simonw-llm.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 659d | 4d | | Open issues (now) | 31 | 689 | | Stars delta | +11 (30d) | +149 (30d) | | Open issues delta | 0 (30d) | +25 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/01-ai-yi/trust.md) | [trust report](/tools/simonw-llm/trust.md) | ## Shared compatibility - **Python**: [Yi](/tools/01-ai-yi.md) - Python runtime; [llm](/tools/simonw-llm.md) - Python runtime ## Decision facts: Yi - **Requirements:** Min 80 GB RAM; Ensure Python 3.10 or later is installed.; Hardware requirements vary depending on the specific Yi model being used. - **Adopt for:** Yi is a series of large language models trained from scratch, offering local deployment and inference capabilities. It is suitable for users who need to run large language models locally and have the necessary hardware,如 ## Decision facts: llm - **Requirements:** - Installation supports multiple methods including `pip`, Homebrew (with caveats noted), `pipx`, and `uv`.; - Requires an OpenAI API key for certain functionalities. - **Adopt for:** Decision-critical facts for 'llm' - **License detail:** Apache-2.0 ## Choose when ### Choose Yi if… - Yi is primarily Jupyter Notebook; llm is Python. - Requirements: Min 80 GB RAM; Ensure Python 3.10 or later is installed.; Hardware requirements vary depending on the specific Yi model being used.. - Tags unique to Yi: inference, jupyter-notebook, large-language-models, local-deployment. - Yi ships Docker support for self-hosted deployment. - Use Yi if you require a large language model that can be deployed and run locally, especially on hardware like the A800 (80G). ### Choose llm if… - llm is primarily Python; Yi is Jupyter Notebook. - Requirements: - Installation supports multiple methods including `pip`, Homebrew (with caveats noted), `pipx`, and `uv`.; - Requires an OpenAI API key for certain functionalities.. - Tags unique to llm: ai, llms, openai. - - You prioritize command-line interaction over graphical interfaces, as llm is designed to provide a seamless CLI experience with multiple installation methods. ## When NOT to use Yi - Avoid Yi if your hardware does not meet the minimum requirements for running the models, such as the A800 (80G) for Yi-34B-Chat. - Do not use Yi if you prefer cloud-based solutions or do not have the capability to install and manage the local environment required for Yi. ## When NOT to use llm - - If you require real-time visual feedback or a graphical interface for interacting with language models, as llm is strictly command-line-based. - - If your primary focus is on model training rather than inference or serving, since llm is aimed at accessing and using pre-trained models. ## Common questions ### What is the difference between Yi and llm? Yi: A series of large language models trained from scratch. llm: Access large language models from the command-line. See the comparison table for live GitHub stats and shared categories. ### When should I choose Yi over llm? Choose Yi over llm when Yi is primarily Jupyter Notebook; llm is Python; Requirements: Min 80 GB RAM; Ensure Python 3.10 or later is installed.; Hardware requirements vary depending on the specific Yi model being used.; Tags unique to Yi: inference, jupyter-notebook, large-language-models, local-deployment; Yi ships Docker support for self-hosted deployment; Use Yi if you require a large language model that can be deployed and run locally, especially on hardware like the A800 (80G). ### When should I choose llm over Yi? Choose llm over Yi when llm is primarily Python; Yi is Jupyter Notebook; Requirements: - Installation supports multiple methods including `pip`, Homebrew (with caveats noted), `pipx`, and `uv`.; - Requires an OpenAI API key for certain functionalities.; Tags unique to llm: ai, llms, openai; - You prioritize command-line interaction over graphical interfaces, as llm is designed to provide a seamless CLI experience with multiple installation methods. ### When should I avoid Yi? Avoid Yi if your hardware does not meet the minimum requirements for running the models, such as the A800 (80G) for Yi-34B-Chat. Do not use Yi if you prefer cloud-based solutions or do not have the capability to install and manage the local environment required for Yi. ### When should I avoid llm? - If you require real-time visual feedback or a graphical interface for interacting with language models, as llm is strictly command-line-based. - If your primary focus is on model training rather than inference or serving, since llm is aimed at accessing and using pre-trained models. ### Is Yi or llm more popular on GitHub? llm has more GitHub stars (12,473 vs 7,833). Stars measure visibility, not whether either tool fits your constraints. ### Are Yi and llm open source? Yes - both are open-source projects on GitHub (Yi: Apache-2.0, llm: Apache-2.0). ### Where can I find alternatives to Yi or llm? GraphCanon lists graph-backed alternatives at [Yi alternatives](/tools/01-ai-yi/alternatives) and [llm alternatives](/tools/simonw-llm/alternatives) ([Yi markdown twin](/tools/01-ai-yi/alternatives.md), [llm markdown twin](/tools/simonw-llm/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/01-ai-yi-vs-simonw-llm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Yi or llm? Yi: Dormant. llm: 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 Yi and llm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Yi trust report](/tools/01-ai-yi/trust); [llm trust report](/tools/simonw-llm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=01-ai-yi`](/api/graphcanon/graph?tool=01-ai-yi) - 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/_