--- title: "optillm vs exllama" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/algorithmicsuperintelligence-optillm-vs-turboderp-exllama" tools: ["algorithmicsuperintelligence-optillm", "turboderp-exllama"] --- # optillm vs exllama *GraphCanon updated Aug 17, 2026* ## Verdict Pick optillm if optillm is an optimizing inference proxy for LLMs that provides enhanced deployment options through Docker, supporting both full and lightweight configurations; pick exllama if exLlama provides a memory-efficient implementation of the LLaMa model with support for quantized weights, primarily aimed at users with NVIDIA GPUs from the 30-series onwards. [optillm](https://github.com/algorithmicsuperintelligence/optillm) reports 4.2k GitHub stars, 385 forks, and 25 open issues, last pushed Jul 18, 2026. [exllama](https://github.com/turboderp/exllama) has 2.9k stars, 220 forks, and 65 open issues, last pushed Sep 30, 2023. Figures are from public GitHub metadata via [optillm's repository](https://github.com/algorithmicsuperintelligence/optillm) and [exllama's repository](https://github.com/turboderp/exllama). | | [optillm](/tools/algorithmicsuperintelligence-optillm.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Tagline | Optimizing inference proxy for LLMs | Memory-efficient rewrite of HF transformers for Llama with quantized weights | | Stars | 4,244 | 2,937 | | Forks | 385 | 220 | | Open issues | 25 | 65 | | Language | Python | Python | | Adopt for | optillm is an optimizing inference proxy for LLMs that provides enhanced deployment options through Docker, supporting both full and lightweight configurations. | ExLlama provides a memory-efficient implementation of the LLaMa model with support for quantized weights, primarily aimed at users with NVIDIA GPUs from the 30-series onwards. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Inference & Serving | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [optillm](/tools/algorithmicsuperintelligence-optillm.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 30d | 1041d | | Open issues (now) | 25 | 65 | | Stars delta | +67 (30d) | Unknown | | Open issues delta | +5 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/algorithmicsuperintelligence-optillm/trust.md) | [trust report](/tools/turboderp-exllama/trust.md) | ## Decision facts: optillm - **Hosting:** self hosted - This open-source proxy supports diverse hosting environments and can be run via Docker for flexibility in deployment. - **Pricing:** freemium - optillm is available under the Apache-2.0 license, which makes it free to use and distribute without cost. - **Adopt for:** optillm is an optimizing inference proxy for LLMs that provides enhanced deployment options through Docker, supporting both full and lightweight configurations. ## Decision facts: exllama - **Adopt for:** ExLlama provides a memory-efficient implementation of the LLaMa model with support for quantized weights, primarily aimed at users with NVIDIA GPUs from the 30-series onwards. ## Choose when ### Choose optillm if… - License: optillm is Apache-2.0, exllama is MIT. - This open-source proxy supports diverse hosting environments and can be run via Docker for flexibility in deployment. - Pricing: optillm is available under the Apache-2.0 license, which makes it free to use and distribute without cost.. - Tags unique to optillm: agent, agentic-ai, genai, llm-inference. - Use optillm when you require automatic optimization of the server approach to enhance reasoning capabilities with large language models. ### Choose exllama if… - License: exllama is MIT, optillm is Apache-2.0. - Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu. - Also covers LLM Frameworks. - - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support. ## When NOT to use optillm - Avoid optillm when your application does not require proxy server optimization for large language models; simpler serving setups may suffice. - Do not use optillm if your deployment environment strictly prohibits the use of Docker images or containers, given that this tool heavily relies on Docker for its various configurations. ## When NOT to use exllama - - If you are operating older GPUs such as Pascal series, which lack robust FP16 support; alternatives like AutoGPTQ might perform better. - - In scenarios that involve AMD GPU hardware (due to limited testing and optimization efforts). ## Common questions ### What is the difference between optillm and exllama? optillm: Optimizing inference proxy for LLMs. exllama: Memory-efficient rewrite of HF transformers for Llama with quantized weights. See the comparison table for live GitHub stats and shared categories. ### When should I choose optillm over exllama? Choose optillm over exllama when License: optillm is Apache-2.0, exllama is MIT; This open-source proxy supports diverse hosting environments and can be run via Docker for flexibility in deployment; Pricing: optillm is available under the Apache-2.0 license, which makes it free to use and distribute without cost.; Tags unique to optillm: agent, agentic-ai, genai, llm-inference; Use optillm when you require automatic optimization of the server approach to enhance reasoning capabilities with large language models. ### When should I choose exllama over optillm? Choose exllama over optillm when License: exllama is MIT, optillm is Apache-2.0; Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu; Also covers LLM Frameworks; - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support. ### When should I avoid optillm? Avoid optillm when your application does not require proxy server optimization for large language models; simpler serving setups may suffice. Do not use optillm if your deployment environment strictly prohibits the use of Docker images or containers, given that this tool heavily relies on Docker for its various configurations. ### When should I avoid exllama? - If you are operating older GPUs such as Pascal series, which lack robust FP16 support; alternatives like AutoGPTQ might perform better. - In scenarios that involve AMD GPU hardware (due to limited testing and optimization efforts). ### Is optillm or exllama more popular on GitHub? optillm has more GitHub stars (4,244 vs 2,937). Stars measure visibility, not whether either tool fits your constraints. ### Are optillm and exllama open source? Yes - both are open-source projects on GitHub (optillm: Apache-2.0, exllama: MIT). ### Where can I find alternatives to optillm or exllama? GraphCanon lists graph-backed alternatives at [optillm alternatives](/tools/algorithmicsuperintelligence-optillm/alternatives) and [exllama alternatives](/tools/turboderp-exllama/alternatives) ([optillm markdown twin](/tools/algorithmicsuperintelligence-optillm/alternatives.md), [exllama markdown twin](/tools/turboderp-exllama/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/algorithmicsuperintelligence-optillm-vs-turboderp-exllama.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, optillm or exllama? optillm: Steady. exllama: Dormant. 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 exllama? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [optillm trust report](/tools/algorithmicsuperintelligence-optillm/trust); [exllama trust report](/tools/turboderp-exllama/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/_