Valérian de Thézan de Gaussan 
As the AI landscape continues to evolve, developers and businesses are constantly seeking ways to optimize their language models and improve performance.
With the recent release of LLaMA3, many are considering migrating from GPT-4 to take advantage of the new model’s capabilities.
In this article, we’ll explore the main good reasons to switch from GPT-4 to LLaMA3 using Groq.
Good reasons to switch
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LLaMA3 and GPT-4 have different model architectures and training data. LLaMA3 is a 70B parameter model trained on a massive dataset of text from the internet, while GPT-4 is a ~8x250B parameter model trained on a proprietary dataset. Having said that, Llama3 is at least as good as GPT-4 according to benchmarks, while having way less parameters i.e. less costly to run.
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Llama3 has been released later so will contain more up-to-date information than GPT-4.
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LLaMA3 running on Groq is very fast. In the article I wrote yesterday, I showed Groq was spitting tokens 24x faster than OpenAI with GPT-4.
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Groq provides an HTTP API for interacting with the models deployed, such as Llama3 and mistral. This API is made to be compatible with the OpenAI one, so that switching should just be about a configuration change.
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Cost of 1M output tokens using Groq and Llama3: 30. Who doesn’t like paying 38x times less?
But there is one thing you should be careful of…
When switching from GPT-4 to LLaMA3, remember that LLaMA3 can only handle a small amount of context - about 6,000 words (8k tokens) or 10-15 pages of text. In contrast, GPT-4 can handle a massive amount of context - about 96,000 words (128k tokens) or 180-220 pages of text! This big difference can affect how well your application works, especially if it needs to process a lot of information at once.
Don’t hesitate to share this tutorial with fellow developers, AI enthusiasts, or anyone interested in exploring the capabilities of LLaMA3 and Groq!