> For the complete documentation index, see [llms.txt](https://docs.zama.org/tfhe-rs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zama.org/tfhe-rs/tfhe-rs/0.10/get-started/summary/gpu_benchmarks.md).

# GPU Benchmarks

This document details the GPU performance benchmarks of homomorphic operations using **TFHE-rs**.

All GPU benchmarks presented here were obtained on H100 GPUs, and rely on the multithreaded PBS algorithm. The cryptographic parameters `PARAM_GPU_MULTI_BIT_MESSAGE_2_CARRY_2_GROUP_3_KS_PBS` were used.

## 1xH100

Below come the results for the execution on a single H100. The following table shows the performance when the inputs of the benchmarked operation are encrypted:

{% embed url="<https://docs.google.com/spreadsheets/d/1dhNYXm7oY0l2qjX3dNpSZKjIBJElkEZtPDIWHZ4FA_A/edit?usp=sharing>" %}

The following table shows the performance when the left input of the benchmarked operation is encrypted and the other is a clear scalar of the same size:

{% embed url="<https://docs.google.com/spreadsheets/d/1wtnFnOwHrSOvfTWluUEaDoTULyveseVl1ZsYo3AOFKk/edit?usp=sharing>" %}

## 2xH100

Below come the results for the execution on two H100's. The following table shows the performance when the inputs of the benchmarked operation are encrypted:

{% embed url="<https://docs.google.com/spreadsheets/d/1_2AUeu3ua8_PXxMfeJCh-pp6b9e529PGVEYUuZRAThg/edit?usp=sharing>" %}

The following table shows the performance when the left input of the benchmarked operation is encrypted and the other is a clear scalar of the same size:

{% embed url="<https://docs.google.com/spreadsheets/d/1nLPt_m1MbkSdhMop0iKDnSN_c605l_JdMpK5JC90N_Q/edit?usp=sharing>" %}

## Programmable bootstrapping

The next table shows the execution time of a keyswitch followed by a programmable bootstrapping depending on the precision of the input message. The associated parameter set is given.

{% embed url="<https://docs.google.com/spreadsheets/d/11JfbPxJ8XMMfob4AZIWhDSglTO9X_YX8R7dNQK73uuk/edit?usp=sharing>" %}


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.zama.org/tfhe-rs/tfhe-rs/0.10/get-started/summary/gpu_benchmarks.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
