Unleashing GPT-4: How Long Does It Take to Process and Generate 90,000 Words?

In the age of advanced AI language models, we're often left in awe of their capabilities. GPT-4, the latest iteration of these models, has the remarkable ability to process vast amounts of text and generate coherent responses. But just how long does it take for GPT-4 to work its magic on a substantial text corpus? In this article, we'll explore the intricacies of language processing with GPT-4 and discover how it handles the task of handling 90,000 words – a novel's worth of text.

Tokens and Time: The Basics

Before we delve into the time it takes for GPT-4 to process a significant amount of text, let's understand the concept of tokens. Tokens are the fundamental units of language that GPT-4 uses to process and generate text. They can be as short as a single character or as long as a word. When you feed a text into GPT-4, it divides it into these tokens, which are then analyzed and processed.

Now, when we talk about processing time, it's important to know that GPT-4 is remarkably efficient. For instance, processing a book with 500 pages and a whopping 90,000 words might seem like an arduous task for a machine, but it takes approximately four hours. Breaking this down, we're looking at around 120,000 tokens being processed in roughly 14,400 seconds.

Doing the math, this translates to about 8.3 tokens processed per second (t/s) or 500 tokens per minute (TPM). However, it's worth noting that this calculation assumes the output translation has a similar number of tokens as the input text.

Tokens: The Great Equalizer

It's essential to recognize that not all languages are equal when it comes to token usage. For languages other than English, it's a well-documented fact that they often require more tokens to convey the same information. In some cases, translating 120,000 input tokens could result in an output of 150,000 to even 500,000 tokens. This variation in token expansion makes the processing speeds even more remarkable.

Speed vs. Aspirations

Now, let's address the speed question: Is GPT-4 slow? The answer is not a straightforward "yes" or "no." Rather, it's a matter of expectation. Given the immense potential of this technology, we often wish it could work even faster. However, labeling it as "slow" would be an unfair assessment of its capabilities.

Boosting Efficiency: Splitting Text and Simultaneous Requests

If you find yourself in a situation where faster processing is a necessity, there are ways to optimize your workflow with GPT-4. One highly effective approach is to split up your text and send simultaneous requests to the model. By doing this, you can distribute the processing load across multiple threads, significantly boosting the overall speed.

Calculating for Efficiency

Let's break down how this optimization can substantially reduce processing time. Suppose you're translating to a language that has a token density twice that of your input language. In this case, the total number of tokens you'd be using is three times your original token count, amounting to 360,000 tokens.

The API has a rate limit of 350,000 TPM. In theory, this means you could process the entire text in just over a minute. To achieve this, you'd want to utilize multiple threads, each sending around 250 tokens in each request.

Avoiding Errors and Delays

To prevent hitting the rate limit and encountering errors, it's advisable to space out sending each request by approximately 50 milliseconds. In total, including the overhead of splitting up the text, this optimized approach would take about 90 seconds, a remarkable improvement in processing time.

Conclusion: Harnessing the Speed of GPT-4

In conclusion, the speed at which GPT-4 processes text is truly impressive, considering the complexity of language it handles. While it may not always meet our desires for instantaneous results, it's essential to recognize that it's far from slow. By understanding its capabilities and optimizing your workflow, you can harness the full potential of this powerful language model and navigate the delicate balance between speed and efficiency.