Comparison
TinyZero vs exllama
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.
Markdown twin · TinyZero alternatives · exllama alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | TinyZero | exllama |
|---|---|---|
| Maintenance | Slowing (160d since push) As of 2w · github_public_v1 | Dormant (1041d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | No published findings from this source as of 2026-07-11 As of 1mo · osv@v1 | Published findings As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- TinyZero
- Minimal reproduction of DeepSeek R1-Zero
- exllama
- Memory-efficient rewrite of HF transformers for Llama with quantized weights
Stars
- TinyZero
- 13k
- exllama
- 2.9k
Forks
- TinyZero
- 1.6k
- exllama
- 220
Open issues
- TinyZero
- 82
- exllama
- 65
Language
- TinyZero
- Python
- exllama
- Python
Adopt for
- TinyZero
- TinyZero is a scaled-down version of the R1-Zero architecture from DeepSeek, focusing on minimal setup with essential components.
- exllama
- 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
- TinyZero
- -
- exllama
- -
Runtime
- TinyZero
- -
- exllama
- -
License
- TinyZero
- TinyZero is licensed under Apache-2.0, allowing for broad usage with attribution requirements.
- exllama
- MIT
Last pushed
- TinyZero
- Feb 27, 2026
- exllama
- Sep 30, 2023
Categories
- TinyZero
- LLM Frameworks
- exllama
- Inference & Serving, LLM Frameworks
Trust and health
Maintenance
- TinyZero
- Slowing (36%)
- exllama
- Dormant (18%)
Days since push
- TinyZero
- 160d
- exllama
- 1041d
Open issues (now)
- TinyZero
- 82
- exllama
- 65
OSV dependency advisories
- TinyZero
- No published findings from this source as of 2026-07-11
- exllama
- Published findings
Full report
- TinyZero
- Trust report
- exllama
- Trust report
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.
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.
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 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).
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Jiayi-Pan/TinyZero) · observed Aug 6, 2026
- GitHub forks (Jiayi-Pan/TinyZero) · observed Aug 6, 2026
- Last push (Jiayi-Pan/TinyZero) · observed Feb 27, 2026
- License file (Apache-2.0) · observed Aug 6, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (turboderp/exllama) · observed Aug 7, 2026
- GitHub forks (turboderp/exllama) · observed Aug 7, 2026
- Last push (turboderp/exllama) · observed Sep 30, 2023
- License file (MIT) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: TinyZero 13k · exllama 2.9k (synced Aug 6, 2026).
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 and exllama alternatives (TinyZero markdown twin, exllama markdown twin), 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 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; exllama trust report.