--- title: "optillm vs airllm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/algorithmicsuperintelligence-optillm-vs-lyogavin-airllm" tools: ["algorithmicsuperintelligence-optillm", "lyogavin-airllm"] --- # optillm vs airllm Neutral, constraint-first comparison with live GitHub stats. | | [optillm](/tools/algorithmicsuperintelligence-optillm.md) | [airllm](/tools/lyogavin-airllm.md) | | --- | --- | --- | | Tagline | Optimizing inference proxy for LLMs | AirLLM for large language model inference on lightweight GPUs | | Stars | 4,172 | 22,274 | | Forks | 369 | 2,560 | | Open issues | 21 | 106 | | Language | Python | Jupyter Notebook | | Adopt for | OptiLLM is an optimizing inference proxy for Large Language Models (LLMs) that improves accuracy and performance on reasoning tasks by implementing state-of-the-art techniques without the need for model training. | AirLLM is a tool designed to dramatically reduce inference memory usage for large language models (LLMs), enabling them to run on lightweight GPUs. It supports running large models like AirLLM 70B, Llama3.1 (405B), and Q | | Persona | - | - | | Runtime | - | - | | License | OptiLLM uses the Apache-2.0 license. | Apache-2.0 | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [optillm](/tools/algorithmicsuperintelligence-optillm.md) | [airllm](/tools/lyogavin-airllm.md) | | --- | --- | --- | | Days since push | 4d | 0d | | Open issues (now) | 21 | 106 | | Owner type | Organization | User | | Security scan | 1 low (1 low) | 4 low (4 low) | | Full report | [trust report](/tools/algorithmicsuperintelligence-optillm/trust.md) | [trust report](/tools/lyogavin-airllm/trust.md) | **Typed relationship:** optillm _(alternative)_ airllm OptiLLM and airllm both target improving the efficiency of large language model inference, but they serve slightly different niches; OptiLLM optimizes accuracy through various techniques without retraining, whereas airllm aims to enable lightweight GPU setups. ## Shared compatibility - **Python**: [optillm](/tools/algorithmicsuperintelligence-optillm.md) - Python runtime; [airllm](/tools/lyogavin-airllm.md) - Python runtime ## Decision facts: optillm - **Requirements:** Works with multiple LLM providers including OpenAI, Anthropic, Google, Cerebras, and supports over 100 models via LiteLLM. - **Adopt for:** OptiLLM is an optimizing inference proxy for Large Language Models (LLMs) that improves accuracy and performance on reasoning tasks by implementing state-of-the-art techniques without the need for model training. - **License detail:** OptiLLM uses the Apache-2.0 license. ## Decision facts: airllm - **Adopt for:** AirLLM is a tool designed to dramatically reduce inference memory usage for large language models (LLMs), enabling them to run on lightweight GPUs. It supports running large models like AirLLM 70B, Llama3.1 (405B), and Q ## Choose when ### Choose optillm if… - optillm is primarily Python; airllm is Jupyter Notebook. - Requirements: Works with multiple LLM providers including OpenAI, Anthropic, Google, Cerebras, and supports over 100 models via LiteLLM.. - OptiLLM and airllm both target improving the efficiency of large language model inference, but they serve slightly different niches; OptiLLM optimizes accuracy through various techniques without retraining, whereas airllm aims to enable lightweight GPU setups. - Tags unique to optillm: optimization, proxy-server, agentic-ai, openai. - optillm ships Docker support for self-hosted deployment. - You require significant improvements in the accuracy of LLMs on math, coding, or logical reasoning tasks as OptiLLM promises 2-10x better accuracy with no additional training. ### Choose airllm if… - airllm is primarily Jupyter Notebook; optillm is Python. - OptiLLM and airllm both target improving the efficiency of large language model inference, but they serve slightly different niches; OptiLLM optimizes accuracy through various techniques without retraining, whereas airllm aims to enable lightweight GPU setups. - Tags unique to airllm: llama, chinese-llm, llm, instruct-gpt. - You should use AirLLM if you need to run very large models such as Qwen3-235B or DeepSeek-V3 (671B) on lower-end GPUs like a single 3GB, 8GB, or ~12GB card without resorting to quantization, distill ## When NOT to use optillm - If you are looking for a solution that requires minimal compute overhead at inference time, as OptiLLM achieves its improvements by performing additional computations during this phase. - When the core functionality or training of LLMs must be altered to achieve accuracy improvements, since OptiLLM operates as an external proxy and does not involve any direct modifications to the model ## When NOT to use airllm - Avoid using AirLLM if you require running models that are not supported by the tool or if your inference environment does not align with its lightweight GPU requirements. If your infrastructure can n ## Common questions ### What is the difference between optillm and airllm? optillm: Optimizing inference proxy for LLMs. airllm: AirLLM for large language model inference on lightweight GPUs. See the comparison table for live GitHub stats and shared categories. ### When should I choose optillm over airllm? Choose optillm over airllm when optillm is primarily Python; airllm is Jupyter Notebook; Requirements: Works with multiple LLM providers including OpenAI, Anthropic, Google, Cerebras, and supports over 100 models via LiteLLM.; OptiLLM and airllm both target improving the efficiency of large language model inference, but they serve slightly different niches; OptiLLM optimizes accuracy through various techniques without retraining, whereas airllm aims to enable lightweight GPU setups; Tags unique to optillm: optimization, proxy-server, agentic-ai, openai; optillm ships Docker support for self-hosted deployment; You require significant improvements in the accuracy of LLMs on math, coding, or logical reasoning tasks as OptiLLM promises 2-10x better accuracy with no additional training. ### When should I choose airllm over optillm? Choose airllm over optillm when airllm is primarily Jupyter Notebook; optillm is Python; OptiLLM and airllm both target improving the efficiency of large language model inference, but they serve slightly different niches; OptiLLM optimizes accuracy through various techniques without retraining, whereas airllm aims to enable lightweight GPU setups; Tags unique to airllm: llama, chinese-llm, llm, instruct-gpt; You should use AirLLM if you need to run very large models such as Qwen3-235B or DeepSeek-V3 (671B) on lower-end GPUs like a single 3GB, 8GB, or ~12GB card without resorting to quantization, distill. ### When should I avoid optillm? If you are looking for a solution that requires minimal compute overhead at inference time, as OptiLLM achieves its improvements by performing additional computations during this phase. When the core functionality or training of LLMs must be altered to achieve accuracy improvements, since OptiLLM operates as an external proxy and does not involve any direct modifications to the model ### When should I avoid airllm? Avoid using AirLLM if you require running models that are not supported by the tool or if your inference environment does not align with its lightweight GPU requirements. If your infrastructure can n ### Is optillm or airllm more popular on GitHub? airllm has more GitHub stars (22,274 vs 4,172). Stars measure visibility, not whether either tool fits your constraints. ### Are optillm and airllm open source? Yes - both are open-source projects on GitHub (optillm: Apache-2.0, airllm: Apache-2.0). ### Where can I find alternatives to optillm or airllm? GraphCanon lists graph-backed alternatives at /tools/algorithmicsuperintelligence-optillm/alternatives and /tools/lyogavin-airllm/alternatives (/tools/algorithmicsuperintelligence-optillm/alternatives.md, /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 /compare/algorithmicsuperintelligence-optillm-vs-lyogavin-airllm.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, optillm or airllm? optillm: Very active. 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 optillm and airllm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: optillm: /tools/algorithmicsuperintelligence-optillm/trust; airllm: /tools/lyogavin-airllm/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=algorithmicsuperintelligence-optillm`](/api/graphcanon/graph?tool=algorithmicsuperintelligence-optillm) - 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/_