AI chip

Extracted Attributes

• Type

  Specialized integrated circuit

• Purpose

  Handle AI tasks, machine learning, data analysis, natural language processing

• Components

  Semiconductor (usually silicon), transistors

• Common Types

  Graphics Processing Units (GPUs), Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs)

• Market Status

  High demand, intense global competition

• DeepSeek V3 Training Cost

  US$6 million (reported)

• Trade Restrictions Impact

  Chinese companies like DeepSeek innovate using weaker AI chips due to export restrictions

• Architectural Requirements

  Optimal processors, memory arrays, security, real-time data connectivity

• DeepSeek V3 Computing Power

  Approximately one-tenth of Meta's Llama 3.1 (reported)

• Primary Bottleneck for Growth

  Escalating energy consumption for AI

Timeline

• Graphcore, a British AI chip company, founded. (Source: Web Search Results)

  2016

• Axelera AI, specializing in AI hardware acceleration, founded in Eindhoven, Netherlands. (Source: Web Search Results)

  2021-07

• DeepSeek, a Chinese artificial intelligence company, founded by Liang Wenfeng. (Source: Wikipedia)

  2023-07

• OpenAI's GPT-4 model reported to cost US$100 million to train. (Source: Wikipedia)

  2023

• The Wall Street Journal published 'How Chips That Power AI Work' video, explaining the technology behind generative AI chips. (Source: Web Search Results)

  2023-12-27

• DeepSeek launched its eponymous chatbot alongside its DeepSeek-R1 model. (Source: Wikipedia)

  2025-01

DeepSeek

Hangzhou DeepSeek Artificial Intelligence Basic Technology Research Co., Ltd., doing business as DeepSeek, is a Chinese artificial intelligence company that develops large language models (LLMs). Based in Hangzhou, Zhejiang, Deepseek is owned and funded by the Chinese hedge fund High-Flyer. DeepSeek was founded in July 2023 by Liang Wenfeng, the co-founder of High-Flyer, who also serves as the CEO for both of the companies. The company launched an eponymous chatbot alongside its DeepSeek-R1 model in January 2025. Released under the MIT License, DeepSeek-R1 provides responses comparable to other contemporary large language models, such as OpenAI's GPT-4 and o1. Its training cost was reported to be significantly lower than other LLMs. The company claims that it trained its V3 model for US$6 million—far less than the US$100 million cost for OpenAI's GPT-4 in 2023—and using approximately one-tenth the computing power consumed by Meta's comparable model, Llama 3.1. DeepSeek's success against larger and more established rivals has been described as "upending AI". DeepSeek's models are described as "open weight," meaning the exact parameters are openly shared, although certain usage conditions differ from typical open-source software. The company reportedly recruits AI researchers from top Chinese universities and also hires from outside traditional computer science fields to broaden its models' knowledge and capabilities. DeepSeek significantly reduced training expenses for their R1 model by incorporating techniques such as mixture of experts (MoE) layers. The company also trained its models during ongoing trade restrictions on AI chip exports to China, using weaker AI chips intended for export and employing fewer units overall. Observers say this breakthrough sent "shock waves" through the industry, threatening established AI hardware leaders such as Nvidia; Nvidia's share price dropped sharply, losing US$600 billion in market value, the largest single-company decline in U.S. stock market history.

Web Search Results

• AI Chips: What Are They? - Built In

  An AI chip is a specialized integrated circuit designed to handle AI tasks. Graphics processing units (GPUs), field programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs) are all considered AI chips. [...] The term “AI chip” is a broad classification, encompassing various chips designed to handle the uniquely complex computational requirements of AI algorithms quickly and efficiently. This includes graphics processing units (GPUs), field-programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs). Central processing units (CPUs) can also be used in simple AI tasks, but they are becoming less and less useful as the industry advances. ## How Do AI Chips Work? [...] Built In Logo Built In Logo Company Photo # AI Chips: What Are They? An AI chip is an integrated circuit designed specifically for use in AI systems and tasks. The future of artificial intelligence largely hinges on the development of AI chips. Ellen Glover Image of a computer chip with various blue nodes and lines coming out of it, indicating connectivity.

• What is an AI chip? - IBM

  The term AI chip refers to an integrated circuit unit that is built out of a semiconductor (usually silicon) and transistors. Transistors are semiconducting materials that are connected to an electronic circuit. When an electrical current is sent through the circuit and turned on and off, it makes a signal that can be read by a digital device as a one or a zero. [...] # What is an AI chip? ## Authors Author, IBM Think Senior Editorial Strategist ## What is an AI chip? Artificial intelligence (AI) chips are specially designed computer microchips used in the development of AI systems. Unlike other kinds of chips, AI chips are often built specifically to handle AI tasks, such as machine learning (ML), data analysis and natural language processing (NLP). [...] The term “AI chip” is broad and includes many kinds of chips designed for the demanding compute environments required by AI tasks. Examples of popular AI chips include graphics processing units (GPUs), field programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs). While some of these chips aren’t necessarily designed specifically for AI, they are designed for advanced applications and many of their capabilities are applicable to AI workloads. Industry newsletter

• What is AI Chip Design? – How it Works - Synopsys

  AI-driven chip design involves the use of artificial intelligence (AI) technologies such as machine learning in the tool flow to design, verify, and test semiconductor devices. For example, the solution space for finding the optimal power, performance, and area (PPA) for chips is quite large. There is a substantial number of input parameters that can be varied and lead to different results. Essentially, it is not humanly possible to explore all these combinations to find the best results in a [...] AI workloads are massive, demanding a significant amount of bandwidth and processing power. As a result, AI chips require a unique architecture consisting of the optimal processors, memory arrays, security, and real-time data connectivity. Traditional CPUs typically lack the processing performance needed, but are ideal for performing sequential tasks. GPUs, on the other hand, can handle the massive parallelism of AI’s multiply-accumulate functions and can be applied to AI applications. In fact, [...] AI accelerators are another type of chip optimized for AI workloads, which tend to require instantaneous responses. A high-performance parallel computation machine, an AI accelerator can be used in large-scale deployments such as data centers as well as space- and power-constrained applications such as edge AI.

• Top 20 AI Chip Makers: NVIDIA & Its Competitors in 2025

  Image 30 As seen above, increasing number of parameters, dataset size and compute led generative AI models to become more accurate. To build better deep learning models and power generative AI applications, organizations require increased computing power and memory bandwidth. Powerful general-purpose chips (such as CPUs) cannot support highly parallelized deep learning models. Therefore, AI chips (e.g. GPUs) that enable parallel computing capabilities are increasingly in demand. [...] Founded in July 2021 in Eindhoven, Netherlands, Axelera AI specializes in AI hardware acceleration technology for computer vision and generative AI. The company is developing Titania, an AI inference chiplet based on its Digital In-Memory Computing (D-IMC) architecture, designed to accelerate AI workloads from edge to cloud. [...] Graphcore is a British company founded in 2016. The company announced its flagship AI chip as IPU-POD256. Graphcore has already been funded with around $700 million. Company has strategic partnerships between data storage corporations like DDN, Pure Storage and Vast Data. Graphcore’s AI chips serve research institutes like Oxford-Man Institute of Quantitative Finance, University of Bristol and Berkeley University of California.

• How Chips That Power AI Work | WSJ Tech Behind - YouTube

  WSJ explains how these AI chips work, how they differ from normal computer chips and whether the tech will have a long lifespan. Chapters: 0:00 Generative AI chips 0:45 Amazon’s chip lab 2:18 Breakdown of the tech 4:18 The market Tech Behind 'The Tech Behind' explores the amazing engineering, computing, science and algorithms that power our favorite tech. #AI #Chips #WSJ 193 comments [...] # How Chips That Power AI Work | WSJ Tech Behind The Wall Street Journal 8011 likes 432279 views 27 Dec 2023 The technology behind generative AI like ChatGPT has exploded, fueling a demand for chips that can handle the complex processing power these programs need. Big tech companies Microsoft, Amazon and Google are all designing their own chips because they can optimize their computing workloads for the software that runs on their cloud. But what does the future of the industry look like?