--- title: "TinyZero vs exllama" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/jiayi-pan-tinyzero-vs-turboderp-exllama" tools: ["jiayi-pan-tinyzero", "turboderp-exllama"] --- # TinyZero vs exllama *GraphCanon updated Aug 7, 2026* ## Verdict Pick TinyZero if tinyZero is a scaled-down version of the R1-Zero architecture from DeepSeek, focusing on minimal setup with essential components; 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. [TinyZero](https://github.com/Jiayi-Pan/TinyZero) reports 13k GitHub stars, 1.6k forks, and 82 open issues, last pushed Feb 27, 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 [TinyZero's repository](https://github.com/Jiayi-Pan/TinyZero) and [exllama's repository](https://github.com/turboderp/exllama). | | [TinyZero](/tools/jiayi-pan-tinyzero.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Tagline | Minimal reproduction of DeepSeek R1-Zero | Memory-efficient rewrite of HF transformers for Llama with quantized weights | | Stars | 13,214 | 2,937 | | Forks | 1,581 | 220 | | Open issues | 82 | 65 | | Language | Python | Python | | Adopt for | TinyZero is a scaled-down version of the R1-Zero architecture from DeepSeek, focusing on minimal setup with essential components. | 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 | TinyZero is licensed under Apache-2.0, allowing for broad usage with attribution requirements. | MIT | | Categories | LLM Frameworks | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [TinyZero](/tools/jiayi-pan-tinyzero.md) | [exllama](/tools/turboderp-exllama.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 160d | 1041d | | Open issues (now) | 82 | 65 | | Full report | [trust report](/tools/jiayi-pan-tinyzero/trust.md) | [trust report](/tools/turboderp-exllama/trust.md) | ## Decision facts: TinyZero - **Pricing:** freemium - The framework itself is free and can be used without charge; - **Requirements:** Min 4 GB RAM; Specific Python environment setup (Python 3.9) and dependency installation steps are outlined in the README. - **Adopt for:** TinyZero is a scaled-down version of the R1-Zero architecture from DeepSeek, focusing on minimal setup with essential components. - **License detail:** TinyZero is licensed under Apache-2.0, allowing for broad usage with attribution requirements. ## 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 TinyZero if… - License: TinyZero is Apache-2.0, exllama is MIT. - Pricing: The framework itself is free and can be used without charge;. - Requirements: Min 4 GB RAM; Specific Python environment setup (Python 3.9) and dependency installation steps are outlined in the README.. - Tags unique to TinyZero: deepseek, r1-zero, ray, vllm. - When you need a streamlined implementation of the R1-Zero architecture without unnecessary complexity. ### Choose exllama if… - License: exllama is MIT, TinyZero is Apache-2.0. - Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu. - Also covers Inference & Serving. - exllama ships Docker support for self-hosted deployment. - - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support. ## When NOT to use TinyZero - If your project demands extensive customization options not available in this minimal version. - When working with environments where specific versions of PyTorch older than 2.4.0 are required, as TinyZero mandates the use of PyTorch 2.4.0 or allows vLLM to manage its installation. ## 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 TinyZero and exllama? TinyZero: Minimal reproduction of DeepSeek R1-Zero. 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 TinyZero over exllama? Choose TinyZero over exllama when License: TinyZero is Apache-2.0, exllama is MIT; Pricing: The framework itself is free and can be used without charge;; Requirements: Min 4 GB RAM; Specific Python environment setup (Python 3.9) and dependency installation steps are outlined in the README.; Tags unique to TinyZero: deepseek, r1-zero, ray, vllm; When you need a streamlined implementation of the R1-Zero architecture without unnecessary complexity. ### When should I choose exllama over TinyZero? Choose exllama over TinyZero when License: exllama is MIT, TinyZero is Apache-2.0; Tags unique to exllama: docker, llama model, memory-efficient, nvidia gpu; Also covers Inference & Serving; exllama ships Docker support for self-hosted deployment; - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support. ### When should I avoid TinyZero? If your project demands extensive customization options not available in this minimal version. When working with environments where specific versions of PyTorch older than 2.4.0 are required, as TinyZero mandates the use of PyTorch 2.4.0 or allows vLLM to manage its installation. ### 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 TinyZero or exllama more popular on GitHub? TinyZero has more GitHub stars (13,214 vs 2,937). Stars measure visibility, not whether either tool fits your constraints. ### Are TinyZero and exllama open source? Yes - both are open-source projects on GitHub (TinyZero: Apache-2.0, exllama: MIT). ### Where can I find alternatives to TinyZero or exllama? GraphCanon lists graph-backed alternatives at [TinyZero alternatives](/tools/jiayi-pan-tinyzero/alternatives) and [exllama alternatives](/tools/turboderp-exllama/alternatives) ([TinyZero markdown twin](/tools/jiayi-pan-tinyzero/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/jiayi-pan-tinyzero-vs-turboderp-exllama.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, TinyZero or exllama? TinyZero: Slowing. 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 TinyZero and exllama? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [TinyZero trust report](/tools/jiayi-pan-tinyzero/trust); [exllama trust report](/tools/turboderp-exllama/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=jiayi-pan-tinyzero`](/api/graphcanon/graph?tool=jiayi-pan-tinyzero) - 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/_