ChatGPT is one of the famous Artificial Intelligence chatbots developed by OpenAI. Many people use ChatGPT in their daily lives for different purposes, such as to answer questions, write essays and emails, for coding, and to learn something new. ChatGPT isn’t a usual AI bot having search engine, but a large language model (LLM) that can be used for different tasks. But do you know how ChatGPT answers your queries? How inference latency in ChatGPT works? Have you ever thought about its backend process? For sure, you certainly haven’t.

You must have faced some technical problems while using ChatGPT. One of the major issues is when AI gets stuck or takes more time to answer your inputs. This taking time is technically known as inference latency to receive your input and respond accordingly. The number of seconds or milliseconds an AI model takes for the entire process is the inference latency.

As an AI user, you should know how to fix this latency and how to improve the model for communication. So, here, you will learn how to fix inference latency in ChatGPT with just 5 quick steps. Keep in mind that if you don’t fix this, your user experience will be bad. Moreover, as a developer, if your AI system is slow, people will abandon it and move to a faster alternative.

Step 1: Use Well Prompt Engineering and Max Tokens

Using well prompt engineering and max tokens directly affects the latency time because LLMs are autoregressive. They can generate only one word at a time, such as in milliseconds or nanoseconds. When you do prompt with max tokens, for example, max_tokens: 50, the model will stop at the fix length for answering. This can enhance the response time 10x faster. If you need only a yes/no answer, then set your limit to only 10 or 20 tokens.

If you use ChatGPT with good prompt engineering, it will not only enhance the quality of response but also improve the answering speed. You can use these methods for the best prompts when using ChatGPT:

• Be with direct instructions: If your prompt is not properly structured, the AI model will take it as filler text. So, instruct with something like, “directly answer my question without any introductory text.”
• Write correct output format: When you write your output format, such as JSON, list, or 3-word summary, the search space of ChatGPT will be less usable. So, use it as models know where to stop.
• Use few-shot prompting: Instead of zero-shot inputs, you should follow the habit of few-shot. In this prompting, you will give ChatGPT some examples or templates, and it will provide you with the best answer without taking extra processing time.

Note: There are two types of latency in AI inference, one is TTFT (Time to First Token), which means understanding the prompt, and the second is TPOT (Time per Output Token), which means time to write every new word.

Step 2: Choose the Right Model for Answers

There is a rule in the Artificial Intelligence world that the biggest model isn’t the best for every job. You should choose the right model of ChatGPT to get the best answers according to your expectations. Actually, latency depends on model parameters, and the core mind of an AI model lies in it. If we talk about big models of ChatGPT, such as GPT-4, it contains billions of parameters, around 70B. In comparison, small models like GPT-4o-mini uses less calculations due to low parameters (8B). The simple rule of thumb is that if you need to summarize text or write an email, using GPT-4 is like taking a truck to buy vegetables. Quite funny, but makes sense! A small bike (GPT-4o-mini) will be faster and cheaper there.

As an AI user, you know that some models are general-purpose and some are specialized. Let’s say, if you want to code, you can use Code Llama, GPT-4o, or DeepSeek-Coder. Despite being small, these AI models outperform larger models in their specific tasks and also maintain low latency. In short, you must follow the trade-off between inference latency and accuracy.

Step 3: Use Streaming to Lower Latency in ChatGPT

Are you aware of AI streaming? When a user calls an API in ChatGPT, the model generates tokens and immediately sent to the user one by one. In this model is still thinking about the next word, but the first word has already appeared on the user’s screen. Your screen will not be blanked. This concept is just like a stream of water when it’s flowing. Conversely, if we talk about non-streaming, an AI model prepares the entire process on the backend and then sends the complete response to you in one go. Here, you will see your screen blank for 5-15 seconds.

Streaming follows the Server-Sent Events (SSE) protocol. The request will be sent, and then a secure connection will be made between server and client. After that, ChatGPT or any AI model will create small chunks of words to send to the screen. At the end, the user experiences the live AI response. One thing to keep in mind is that streaming doesn’t reduce the total processing time of the model. If it takes 5 seconds to write 100 words, the model will actually take 5 seconds. However, because the user gets the first word instantly, they don’t feel the latency.

Step 4: Semantic Caching (Save Previous Responses)

Semantic caching is a smart way to reduce inference latency in ChatGPT. Semantic caching understands the meaning of a question. To give an example, if a user asks “the weather of London right now” and the second user asks the model “what’s the weather of London?”, semantic caching understands that the purpose of both questions is the same. What it actually does is, instead of calling the model, it shows the user’s pre-saved answer. Comparatively, a simple cache will treat the second question as a new question and contact the GPT again.

Semantic caching typically converts the user’s inquiry into mathematical numbers or vectors that contain the question’s context. This number is stored on a specific database such as Redis, Pinecone, or Milvus. When a new input comes, the system checks if the mathematical numbers of the latest question are available on databases. If 95% similarity of numbers occurs, the same answer is sent back to follow the semantic caching rule.

A standard API call takes 2 to 5 seconds, while the same response from the semantic cache is sent in just 0.01 seconds. This is how the caching technique can work to reduce the inference latency in ChatGPT.

Step 5: Quantization and Model Distillation

These two techniques can make ChatGPT models more and small and fast with low latency. You know that ChatGPT models work on billions of parameters, and handling these parameters is difficult. That’s why quantization and distillation techniques come into use. Usually, a system needs a space or storage to store mathematical numbers of AI models, and these models use 32-bit floating point, which is more accurate. In quantization, AI developers compress the 32-bit into 8-bit or 4-bit to reduce the model size by up to 8 times. Accuracy depends on exact round-off.

Note: Quantization and model distillation techniques are not for normal AI users but for developers who create these models.

Model distillation when knowledge from an intelligent model is transferred to a smaller and faster model. Let’s suppose we have a bigger model GPT-4 and a small model with 1B parameters. During the training of the small model, it’s instructed to give the same answers as the bigger one does. It not only learns the correct answer but also copies the way of thinking of the larger model. Examples include DistilBERT, which is 60% the size of the original BERT model but remains 97% accuracy and is 40% faster.

In conclusion, you can follow these five quick steps to reduce the inference latency in ChatGPT. As a developer, keep in mind that optimizing inference latency is not just about increasing speed; it’s crucial to improve user experience and to reduce operational costs. Remember, every application has different needs. Sometimes, you have to tolerate a little latency for accuracy, and sometimes speed is everything.