[P] Tutorial: Benchmarking Bark text-to-speech on 26 consumer GPUs - Reading out 144K recipes

In this project, we benchmarked Bark text-to-speech across **26 different consumer GPUs.**

The goal: To get Bark to ***read 144K food recipes*** from [Food.com](https://Food.com)'s recipe dataset.

You can read the full tutorial here: [**https://blog.salad.com/bark-benchmark-text-to-speech/**](https://blog.salad.com/bark-benchmark-text-to-speech/)

Included: Architecture diagram, data preparation, inference server setup, queue worker, setting up container group & compiling the results

Code-blocks included in the tutorial.

**Words per dollar for each GPU:**

https://preview.redd.it/6daqluu3omsb1.png?width=2000&format=png&auto=webp&s=bc4b74fe6ee80c2721ab324eb0d9a2d7c2f7ddb1

Although the latest cards are indeed much faster than older cards at performing the inference, there’s …

architecture benchmarking cards code consumer data data preparation gpu gpus inference machinelearning per project reading recipes server setup speech text text-to-speech tutorial words