--- title: "OneCompression vs llm-pruning-collection" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/fujitsuresearch-onecompression-vs-zlab-princeton-llm-pruning-collection" tools: ["fujitsuresearch-onecompression", "zlab-princeton-llm-pruning-collection"] --- # OneCompression vs llm-pruning-collection *GraphCanon updated Sep 20, 2026* ## Verdict Pick OneCompression if oneCompression is a Python package for compressing large language models using techniques such as quantization, with support for CUDA and MPS for different environments; pick llm-pruning-collection if the llm-pruning-collection repository provides a comprehensive set of large language model pruning methods, along with the necessary training and evaluation scripts for GPUs and TPUs. [OneCompression](https://fujitsuresearch.github.io/OneCompression/) reports 426 GitHub stars, 20 forks, and 8 open issues, last pushed Sep 14, 2026. [llm-pruning-collection](https://github.com/zlab-princeton/llm-pruning-collection) has 72 stars, 9 forks, and 2 open issues, last pushed Apr 20, 2026. Figures are from public GitHub metadata via [OneCompression's repository](https://github.com/FujitsuResearch/OneCompression) and [llm-pruning-collection's repository](https://github.com/zlab-princeton/llm-pruning-collection). | | [OneCompression](/tools/fujitsuresearch-onecompression.md) | [llm-pruning-collection](/tools/zlab-princeton-llm-pruning-collection.md) | | --- | --- | --- | | Tagline | Python package for LLM compression | Collection of LLM pruning methods and training code for GPUs & TPUs. | | Stars | 426 | 72 | | Forks | 20 | 9 | | Open issues | 8 | 2 | | Language | Python | Python | | Adopt for | OneCompression is a Python package for compressing large language models using techniques such as quantization, with support for CUDA and MPS for different environments. | The llm-pruning-collection repository provides a comprehensive set of large language model pruning methods, along with the necessary training and evaluation scripts for GPUs and TPUs. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Inference & Serving, LLM Frameworks, Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [OneCompression](/tools/fujitsuresearch-onecompression.md) | [llm-pruning-collection](/tools/zlab-princeton-llm-pruning-collection.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 3d | 141d | | Open issues (now) | 8 | 2 | | Stars delta | +28 (30d) | +3 (30d) | | Open issues delta | +1 (30d) | 0 (30d) | | Full report | [trust report](/tools/fujitsuresearch-onecompression/trust.md) | [trust report](/tools/zlab-princeton-llm-pruning-collection/trust.md) | ## Decision facts: OneCompression - **Adopt for:** OneCompression is a Python package for compressing large language models using techniques such as quantization, with support for CUDA and MPS for different environments. ## Decision facts: llm-pruning-collection - **Pricing:** freemium - The software is free and open-source, licensed under Apache-2.0, but users must provide their own hardware or use cloud services like Google TPU Research Cloud for computational resources. - **Requirements:** The repository includes pretraining and fine-tuning scripts for both GPU and TPU platforms.; A JAX-based environment is required to run the code in this repository. - **Adopt for:** The llm-pruning-collection repository provides a comprehensive set of large language model pruning methods, along with the necessary training and evaluation scripts for GPUs and TPUs. ## Choose when ### Choose OneCompression if… - License: OneCompression is MIT, llm-pruning-collection is Apache-2.0. - Tags unique to OneCompression: compression, cuda, llm, mps. - Also covers Inference & Serving, LLM Frameworks. - Use OneCompression when you need to compress large language models and have access to a Linux system with an NVIDIA GPU, as it supports CUDA for quantization and vLLM serving. ### Choose llm-pruning-collection if… - License: llm-pruning-collection is Apache-2.0, OneCompression is MIT. - Pricing: The software is free and open-source, licensed under Apache-2.0, but users must provide their own hardware or use cloud services like Google TPU Research Cloud for computational resources.. - Requirements: The repository includes pretraining and fine-tuning scripts for both GPU and TPU platforms.; A JAX-based environment is required to run the code in this repository.. - Tags unique to llm-pruning-collection: jax, llm-evaluation, llm-training, pruning. - Also covers Evaluation & Observability. - When you are working on reducing the size or improving inference speed of large language models using various pruning techniques available in this collection. ## When NOT to use OneCompression - Avoid OneCompression if you are working on a macOS system and require vLLM serving, as it is only supported on Linux with an NVIDIA GPU. - Do not use OneCompression if you are working with Python 3.13 and CUDA, as the first run with `--extra vllm` may take a long time due to the lack of pre-built `xformers` wheels for this combination. - Do not use OneCompression if you need to work with older CUDA versions like `cu118` or `cu121` and require vLLM, as `--extra vllm` is only compatible with `cu130`. ## When NOT to use llm-pruning-collection - Avoid if your project requires a pruning method that is not included in the collection or if the current platform capabilities do not align with your hardware requirements. - Not suitable for those who need tools to train models from scratch rather than focusing on model pruning and optimization techniques. ## Common questions ### What is the difference between OneCompression and llm-pruning-collection? OneCompression: Python package for LLM compression. llm-pruning-collection: Collection of LLM pruning methods and training code for GPUs & TPUs.. See the comparison table for live GitHub stats and shared categories. ### When should I choose OneCompression over llm-pruning-collection? Choose OneCompression over llm-pruning-collection when License: OneCompression is MIT, llm-pruning-collection is Apache-2.0; Tags unique to OneCompression: compression, cuda, llm, mps; Also covers Inference & Serving, LLM Frameworks; Use OneCompression when you need to compress large language models and have access to a Linux system with an NVIDIA GPU, as it supports CUDA for quantization and vLLM serving. ### When should I choose llm-pruning-collection over OneCompression? Choose llm-pruning-collection over OneCompression when License: llm-pruning-collection is Apache-2.0, OneCompression is MIT; Pricing: The software is free and open-source, licensed under Apache-2.0, but users must provide their own hardware or use cloud services like Google TPU Research Cloud for computational resources.; Requirements: The repository includes pretraining and fine-tuning scripts for both GPU and TPU platforms.; A JAX-based environment is required to run the code in this repository.; Tags unique to llm-pruning-collection: jax, llm-evaluation, llm-training, pruning; Also covers Evaluation & Observability; When you are working on reducing the size or improving inference speed of large language models using various pruning techniques available in this collection. ### When should I avoid OneCompression? Avoid OneCompression if you are working on a macOS system and require vLLM serving, as it is only supported on Linux with an NVIDIA GPU. Do not use OneCompression if you are working with Python 3.13 and CUDA, as the first run with `--extra vllm` may take a long time due to the lack of pre-built `xformers` wheels for this combination. Do not use OneCompression if you need to work with older CUDA versions like `cu118` or `cu121` and require vLLM, as `--extra vllm` is only compatible with `cu130`. ### When should I avoid llm-pruning-collection? Avoid if your project requires a pruning method that is not included in the collection or if the current platform capabilities do not align with your hardware requirements. Not suitable for those who need tools to train models from scratch rather than focusing on model pruning and optimization techniques. ### Is OneCompression or llm-pruning-collection more popular on GitHub? OneCompression has more GitHub stars (426 vs 72). Stars measure visibility, not whether either tool fits your constraints. ### Are OneCompression and llm-pruning-collection open source? Yes - both are open-source projects on GitHub (OneCompression: MIT, llm-pruning-collection: Apache-2.0). ### Where can I find alternatives to OneCompression or llm-pruning-collection? GraphCanon lists graph-backed alternatives at [OneCompression alternatives](/tools/fujitsuresearch-onecompression/alternatives) and [llm-pruning-collection alternatives](/tools/zlab-princeton-llm-pruning-collection/alternatives) ([OneCompression markdown twin](/tools/fujitsuresearch-onecompression/alternatives.md), [llm-pruning-collection markdown twin](/tools/zlab-princeton-llm-pruning-collection/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/fujitsuresearch-onecompression-vs-zlab-princeton-llm-pruning-collection.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, OneCompression or llm-pruning-collection? OneCompression: Very active. llm-pruning-collection: Slowing. 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 OneCompression and llm-pruning-collection? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [OneCompression trust report](/tools/fujitsuresearch-onecompression/trust); [llm-pruning-collection trust report](/tools/zlab-princeton-llm-pruning-collection/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=fujitsuresearch-onecompression`](/api/graphcanon/graph?tool=fujitsuresearch-onecompression) - 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/_