Programming

Goodbye ChatGPT, Groq offers better performance. that’s how

Goodbye ChatGPT, Groq offers better performance.  that's how

The document dating back to 2017, with which a group of Google engineers introduced the concept of transformers, was the spark that inspired OpenAI and led the company to launch its GPT generative models (Generative pre-trained transformer), then debuting the ChatGPT chatbot as a major product mainstream. OpenAI’s move placed Google in a “troubled” situation: the company founded by Larry Page and Sergey Brin did not yet seem well disposed for the debut in the market of solutions based onartificial intelligence.

In the last two years we have witnessed a real competition aimed on the one hand at offering users more or less specialized and high-performance generative models, but first and foremost trained on a corpus Of quality datain order to maximize the relevance and adequacy of the results while reducing the phenomenon of hallucinations (common to all models that use a stochastic approach).

Groq Chatbot: Outperforms ChatGPT

Groq uses its LPU chips to improve the performance of generative models

Now comes the world of AI-based solutions to shake up Groka company based in Mountain View (like Google) that announces that it has reached a new milestone in the development of language processing unit (LPU) using custom hardware. The company has in fact prepared optimized configurations that allow you to run LLM models (Large Language Models) with extraordinary speed.

Groq, specializing in the development of high-performance chips and software solutions for artificial intelligence, machine learning and computer applications computing advanced, is attracting great attention because it presents itself as a revolutionary platform.

Il chip LPU designed by Groq engineers is described as a fundamental pillar to offer performance by a lot superior compared to traditional GPUs. The chatbot published at this address confirms this: although in the “alpha” version, it offers results much more quickly than ChatGPT and other competitors. With the same LLM (currently Groq does not provide its own solution but is based on the most well-known open source AI models), thanks to its LPU chips, Groq can perform processing up to 10 times faster compared to calculations based on GPU.

How to try the chatbot

From the Groq home page, using the drop-down menu Model at the top left, you can select the specific generative model to use. In this first phase, you can use either: Call 2 70B developed by Meta that a Mixtral 8x7Brealized by Mistral AI.

In the field Enter prompt here, you can obviously enter the input to be transferred to the model (also in Europen). Once you have received the answer, which is usually appropriate, consistent and well-argued, you can click on Modify to change the tone of the response, from informal to more formal/professional.

Modifica output chatbot Groq

You can even ask for the generation of a bulleted list that summarize the contents most important of the text, extend the content, create a table as well as several other variations.

As expected, Groq is keen to show the data in real time performance processing by flaunting data such as the number of tokens processed, the calculation speed and the inference time (both in the input and output phases).

Groq AI performance comparison

Integrate Groq into your applications

With the specific intention of encouraging the use of Groq in user applications, the company makes available API (Application Programming Interface) which allow you to communicate with the LPU chips and the underlying LLM models.

Il free trial period of 10 days provides 1 million tokens, which can be freely used to verify Groq’s abilities and integrate the system into your own projects. Furthermore, the “smart” thing is that Groq is compatible with the OpenAI API structure. This means that migrare da OpenAI a Groq it’s as simple as changing a simple connection string.

Groq provides support for standard machine learning frameworks such as PyTorch, TensorFlow e ONNX for inference. This means that the platform is able to perform predictions or inferences using machine learning models already trained on these frameworks.

Currently, Groq does not support themodel training using the LPU-based engine: this implies that, at the moment, the platform is not designed for training new models, but rather for quickly running already trained models.

Wide possibilities for customization, also in terms of optimization of workload

For custom development, Groq provides the suite GroqWarewhich includes the Groq Compiler. It is a solution that guarantees a simplified experience, essential for rapid use of the templates. The Groq Compiler among other things it also allows theworkload optimizationwith the ability to develop “ad hoc” code for the Groq architecture and obtain detailed control at the processor level.

Groq is an application-specific computing (ASIC) company that has created a processor designed specifically for running artificial intelligence and machine learning workloads. Here are some of the benefits of using Groq:

I advantages of using Groq include high performance, energy efficiency, ease of programming, scalability, and security. All aspects that make the new platform an interesting choice for artificial intelligence applications and machine learning that require high computing power and excellent energy efficiency.

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