> For the complete documentation index, see [llms.txt](https://docs.inferencelabs.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.inferencelabs.com/zk-ml/benchmarks.md).

# Benchmarks

Leveraging ZK-SNARKs (zero-knowledge succinct non-interactive argument of knowledge), we demonstrate their versatility across a range of applications. Specifically, we illustrate how to utilize ZK-SNARKs to authenticate the execution of machine learning models.\
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**Why EZKL?** 

* Convert an ONNX model into a ZKP
* Apache 2.0 Open Source License
* Inference Labs is a contributor
* Prove + verify at command-line (or binary, contract, WASM)
* Regularly updating with new layers, suitable for compact production models
* Performance improvements

**NanoGPT**\
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A neural network model created by Andrej Karpathy for generating text, similar to GPT-3.  The main purpose is to demonstrate compressing a self-attention model small enough to run efficiently in a browser or other edge contexts.

<https://github.com/karpathy/nanoGPT>\
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**Hardware**\
Google Cloud\
28 cores \
448GB RAM\
500GB SSD\
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**Our Progress**\
The latest update focused on optimizing sparsity issues. Essentially, certain neural networks may contain redundant computations at various stages (consistently producing zeros). These inefficiencies have been addressed and improved in the most recent version.<br>

<figure><img src="/files/XViXLPA4NRnnsBLZL6FC" alt=""><figcaption><p>In the span of less than a month we have observed a 30% increase in efficiency on implementation optimizations alone.</p></figcaption></figure>

<figure><img src="/files/nCvX9c4P0RevktGbDwy6" alt=""><figcaption></figcaption></figure>

**Next Steps**\
Optimize and reduce proving times and memory consumption while generalizing model support.