Artificial intelligence (AI) is rapidly transforming the world around us. From virtual assistants on our phones to self-driving cars, AI is increasingly present in our lives. However, until recently, AI development and deployment was limited to large companies with access to expensive, powerful servers equipped with cutting-edge GPUs. These chips, designed for training deep language models, are at the heart of machine learning, but their high price and energy consumption made them inaccessible to most.
This is where inference chips come in, an emerging technology that promises to democratise access to AI. Unlike training chips, inference chips are optimised to run already-trained AI models.
Once an AI has “learned” the algorithm, it does not need the same computing power to perform repetitive tasks. Smaller, cheaper, and more energy-efficient inference chips are perfect for running these tasks on devices like laptops, smartphones, and even IoT devices.
Imagine the AI learning process as creating a complex instruction manual. Training chips are like the powerful printing presses that produce the first edition of this manual. They are essential for the initial process, but once printed, we don't need a printing press for each copy of the manual. Inference chips are like photocopiers, capable of reproducing the manual (the AI model) efficiently and affordably.
This distinction between training and inference is key to understanding the disruptive potential of inference chips. They allow complex AI models to be run on local devices, without the need for a connection to the cloud or expensive servers. This opens up a range of possibilities for new AI applications in sectors such as healthcare, industry, transportation, and entertainment.
The market for inference chips is experiencing explosive growth. According to forecasts, it is expected to reach multi-billion dollar figures in the next few years, driven by the increasing demand for AI applications in portable devices. This growth is attracting numerous startups and companies looking to challenge NVIDIA's current dominance in the AI chip sector.
Companies like Google, with its TPUs, Intel, with its Habana Gaudi, and Amazon, with its Inferentia, are investing heavily in the development of inference chips. In addition, a vibrant ecosystem of startups is emerging, offering innovative and specialized solutions for different market niches. This competition is beneficial for the industry, driving innovation and reducing costs.
NVIDIA, the undisputed leader in the GPU market for AI training, faces a new challenge with the rise of inference chips. While it maintains a dominant position, the emergence of specialized inference competitors is eroding its market share. New companies offer chips optimized for specific tasks, with performance and energy efficiency superior in some cases to NVIDIA GPUs.
This change in the competitive landscape does not mean the end of NVIDIA, but a reconfiguration of the market. The company is adapting its strategy, investing in the development of its own inference chips and seeking new opportunities in the sector. However, increasing competition is forcing it to innovate and offer more competitive solutions to maintain its leadership.
Inference chips are changing the rules of the game in the world of AI. They allow for a more efficient, accessible and distributed implementation of artificial intelligence, bringing it closer to a wider audience. It is no longer just about large data centers and supercomputers, but about intelligence at the edge, integrated into the devices we use every day.
This paradigm shift has the potential to revolutionize the way we interact with technology. Imagine smartphones capable of making preliminary medical diagnoses, drones that monitor crops in real time, or household appliances that learn our preferences and adapt to our needs. Inference chips are the key to unlocking this future, a future where AI is an accessible tool for everyone.
The inference chip market is a battlefield where the next great AI war is being fought. Companies that manage to master this technology will have a significant advantage in the race to lead the artificial intelligence revolution. And, most importantly, they will bring us closer to a future where AI improves our lives in ways we can only imagine today.
EN 
ES