Comparison: India’s Krutrim AI assistant vs ChatGPT, Google Gemini and Copilot

Rapidly changing technology has made it more important than ever to have access to quick information. Our dependence on AI chatbots is increasing along with our use of smartphones. Ola’s Krutrim AI assistant is a rising star from India that may have gone unnoticed amidst the global AI titans.

I have contrasted the prominent chatbots ChatGPT, Google Gemini, and Microsoft Copilot with India’s Krutrim AI in this essay.

Comparison: India's Krutrim AI assistant vs ChatGPT, Google Gemini and Copilot

I asked Krutrim, ChatGPT, Google Gemini and Microsoft Copilot to: Explain concept of NPU in processors. Let’s see how they responded.


A customised hardware accelerator called a neural processing unit (NPU) is made to maximise the efficiency of machine learning and artificial intelligence (AI) applications. NPUs are particularly made to perform the intricate mathematical calculations needed by deep learning algorithms, in contrast to conventional CPUs and GPUs. When it comes to processing AI tasks like image analysis and natural language processing, they excel. In order to improve the efficiency of neural network applications and free up CPU resources, NPUs are integrated into chipset processors. Artificial intelligence activities can be completed more quickly and artificial neural network operations can be carried out more effectively when NPUs are integrated into bigger SoCs. 

Comparison: India's Krutrim AI assistant vs ChatGPT, Google Gemini and Copilot


An NPU, or Neural Processing Unit, is a specialised processing unit designed to efficiently perform tasks related to artificial intelligence (AI) and machine learning (ML). Traditional processors, like CPUs (Central Processing Units) and GPUs (Graphics Processing Units), are general-purpose and can handle a wide range of tasks, but they might not be optimised for the specific demands of AI and ML computations.

Neural network operations (NPUs) include matrix multiplications and convolutions, which are essential to deep learning algorithms. NPUs are designed to speed up these kinds of computations. These units are usually designed for low precision arithmetic and high parallelism, which are prominent features of calculations using neural networks.

When AI and ML workloads are executed on NPUs instead on general-purpose CPUs, they can be substantially faster. They are frequently found in many different devices, including data centres, smart cameras, smartphones, and autonomous cars, where AI and ML jobs are becoming more and more crucial. Dedicated NPUs can reduce power consumption and increase overall system performance by taking on these activities, which makes them particularly useful for edge computing and mobile applications where energy conservation is critical.

Microsoft Copilot

A specialist processor created especially for carrying out machine learning algorithms is called an NPU (Neural Processing Unit). NPUs, as opposed to conventional CPUs and GPUs, are designed specifically to handle the intricate mathematical operations required by artificial neural networks. The following are some essential NPU points:

Dedicated Acceleration: An NPU’s purpose is to speed up AI and neural network activities. Its emphasis on data-driven parallel computing makes it exceptionally effective at handling AI-related tasks like voice recognition, background blurring in video chats, and object detection in picture or video editing. It can also analyse large amounts of multimedia data, such as photographs and movies.

Integration: The NPU is often built into the primary CPU in consumer devices. This is the case with the new AMD Ryzen 8040-series laptop CPUs and the Intel Core and Core Ultra series. Nonetheless, the NPU may be a stand-alone standalone processor on the motherboard, unrelated to other processing units, in bigger data centres or specialist industrial processes.

Distinction from GPUs: Although a lot of AI and machine learning tasks are executed on GPUs, there’s a crucial difference between GPUs and NPUs. Although GPUs are well-known for their ability to process data in parallel, not all GPUs are created equal when it comes to handling machine learning tasks. However, because NPUs are specifically designed for neural network calculations, they are more effective for AI applications.

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