AI Chip Makers

Top 10 AI Chip Makers of 2024: In-depth Guide

The world of artificial intelligence (AI) is propelled by the power of AI chips, the backbone of deep learning models and generative AI applications. In 2024, the demand for AI chips continues to soar as neural networks grow in complexity, requiring increased computing power and memory bandwidth. As organizations strive to build better AI models, the need for specialized AI chips becomes paramount. In this in-depth guide, we’ll explore the top AI chip makers of 2024 and their contributions to this rapidly evolving industry.

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Which are the Leading AI Chip Producers?

1. Nvidia

Nvidia stands as a pioneer in producing graphics processing units (GPUs) since the 1990s. Their AI chips, including Volta, Xavier, and Tesla, are designed to tackle various business challenges across industries. With the emergence of generative AI, Nvidia has solidified its leadership in the GPU and AI hardware markets, boasting impressive results and valuation. Their flagship AI chips like DGX™ A100 and H100 are tailored for AI training and inference in data centers, offering unparalleled performance. Nvidia’s dominance extends to the cloud GPU infrastructure, making them a preferred choice for AI workloads in the cloud.

2. Advanced Micro Devices (AMD)

AMD is a formidable player in the AI chip market, offering a diverse portfolio of CPU, GPU, and AI accelerator products. Their Alveo U50 data center accelerator card, with 50 billion transistors, showcases their commitment to performance and innovation. AMD’s MI300 for AI training workloads competes head-to-head with Nvidia, attracting startups, enterprises, and tech giants seeking alternative hardware solutions. Collaborations with machine learning companies like Hugging Face demonstrate AMD’s dedication to software optimization, enhancing the efficiency of their hardware.

3. Intel

Intel, renowned for its dominance in the CPU market, enters the AI chip arena with its latest offering, Gaudi3. While Intel’s Xeon CPUs continue to be a staple in data centers, Gaudi3 promises to deliver breakthrough performance for AI workloads. Although benchmarks for Gaudi3 are limited at present, Intel’s reputation for semiconductor development instills confidence in the industry. With its long-standing presence in the market, Intel is poised to make significant strides in the AI chip space in 2024 and beyond.

Which Public Cloud Providers Produce AI Chips?

4. Alphabet / Google Cloud Platform

Google Cloud TPU emerges as a purpose-built machine learning accelerator chip, powering various Google products and services. Its compact design and efficiency make it an ideal choice for AI workloads in the cloud. Additionally, Edge TPU caters to edge devices, further expanding Google’s reach in the AI chip market. The integration of Google Cloud TPU with Google Cloud offers seamless access to powerful AI infrastructure for businesses and developers alike.

5. AWS

AWS, a leader in public cloud services, introduces Tranium chips for model training and Inferentia chips for inference. These purpose-built AI chips leverage AWS’s vast cloud infrastructure to deliver high-performance computing for AI workloads. By offering a comprehensive suite of AI hardware solutions, AWS empowers businesses to harness the full potential of AI for their applications. The strategic move into AI chip production reinforces AWS’s commitment to innovation and customer-centric solutions.

6. IBM

IBM’s deep learning chip, AIU, marks a significant milestone in the company’s AI journey. Designed to power IBM’s watson.x generative AI platform, AIU offers advanced AI processing capabilities for various applications. Leveraging the IBM Telum Processor, AIU demonstrates impressive performance in tasks such as fraud detection. IBM’s focus on merging compute and memory highlights its commitment to efficiency and innovation in AI hardware.

7. Alibaba

Alibaba’s AI chips, such as Hanguang 800, cater to inference tasks in the cloud. While Alibaba’s offerings boast impressive performance, geopolitical considerations may influence adoption among certain organizations. Despite this, Alibaba’s presence in the AI chip market reflects its commitment to providing competitive solutions for customers globally.

Who are the Leading AI Chip Startups?

8. SambaNova Systems

SambaNova Systems emerges as a leading startup in the AI chip industry, focusing on high-performance hardware-software systems for generative AI workloads. Their flagship SN40L chip, coupled with innovative platform-as-a-service offerings, sets them apart in the competitive landscape. With substantial funding and a commitment to sustainability through hardware reuse, SambaNova Systems is poised for success in 2024 and beyond.

  • SambaNova Systems’ SN40L chip offers high-performance computing for generative AI workloads.
  • Platform-as-a-service approach facilitates easier adoption of SambaNova’s systems, promoting hardware reuse.
  • Substantial funding and commitment to sustainability position SambaNova Systems as a frontrunner in the AI chip startup ecosystem.

9. Cerebras Systems

Cerebras Systems distinguishes itself with its groundbreaking AI chip model, Cerebras WSE-2, boasting impressive performance metrics. Partnering with pharmaceutical companies for accelerated genetic and genomic research, Cerebras Systems showcases the transformative potential of its technology. With a focus on innovation and collaboration, Cerebras Systems is driving advancements in AI hardware for critical applications.

10. Groq

Groq, founded by former Google employees, introduces LPUs as a novel AI chip architecture, simplifying adoption for companies. With substantial funding and impressive benchmarks for LLM inference, Groq is rapidly gaining traction in the AI chip market. Their cloud platform’s growing user base and application ecosystem position Groq for significant growth and innovation in the coming years.

  • Groq’s LPUs offer a new model for AI chip architecture, promising ease of adoption and superior performance.
  • Impressive benchmarks for LLM inference demonstrate Groq’s competitive edge in the AI chip market.
  • Growing user base and application ecosystem on Groq’s cloud platform indicate promising growth and innovation opportunities.

What are Upcoming AI Hardware Producers?


Meta Training and Inference Accelerator (MTIA) represents a new family of processors for AI workloads, with Next Gen MTIA showcasing significant performance improvements. While currently for internal usage, MTIA holds potential for powering future enterprise generative AI offerings from Meta. With its cutting-edge technology and strategic vision, Meta is poised to shape the future of AI hardware.

  • Meta’s MTIA processors offer enhanced performance for AI workloads, paving the way for future enterprise offerings.
  • Next Gen MTIA demonstrates Meta’s commitment to innovation and excellence in AI hardware development.
  • Meta’s strategic vision positions it as a key player in shaping the future of AI hardware technology.

Microsoft Azure

Microsoft Azure’s Maia AI Accelerator promises to deliver powerful AI computing capabilities for a range of applications. With its launch in 2023, Maia AI Accelerator underscores Microsoft’s commitment to providing comprehensive AI solutions for its customers. As Microsoft continues to invest in AI hardware, Azure users can expect cutting-edge performance and reliability for their AI workloads.

  • Microsoft Azure’s Maia AI Accelerator offers powerful computing capabilities for AI applications.
  • Launch of Maia AI Accelerator reflects Microsoft’s dedication to innovation and customer-centric solutions.
  • Continued investment in AI hardware underscores Microsoft Azure’s position as a leading provider of AI infrastructure.

What are Other AI Chip Producers?


Graphcore’s IPU-POD256 AI chip represents a significant advancement in AI hardware technology, offering unparalleled performance for a range of applications. With strategic partnerships and collaborations with leading research institutes, Graphcore is driving innovation in AI chip design. Despite financial challenges, Graphcore’s commitment to research and development positions it as a key player in the AI chip market.

  • Graphcore’s IPU-POD256 AI chip delivers exceptional performance for AI applications, supported by strategic partnerships.
  • Collaborations with leading research institutes underscore Graphcore’s commitment to innovation in AI chip design.
  • Despite financial challenges, Graphcore remains a frontrunner in the AI chip market, thanks to its focus on research and development.


Mythic’s focus on edge AI computing and analog compute architecture sets it apart in the AI chip market. Despite challenges, such as restructuring and layoffs, Mythic continues to innovate with products like M1076 AMP. With substantial funding and a unique approach to AI hardware, Mythic holds promise for the future of edge computing and AI applications.

  • Mythic’s analog compute architecture offers power-efficient edge AI computing solutions, driving innovation in the field.
  • Despite challenges, such as restructuring, Mythic remains committed to advancing AI hardware technology.
  • Substantial funding and product innovation position Mythic as a key player in edge computing and AI hardware development.

What are Companies Reported to be Working on AI Hardware?


While _etched claims to have built the world’s first transformer supercomputer, benchmarks and client references are yet to be verified. Despite this, _etched’s innovative approach to AI hardware warrants attention and further investigation. As _etched continues to develop its technology, it may emerge as a significant player in the AI chip market.

  • _etched’s claim of building the world’s first transformer supercomputer sparks interest in the AI hardware community.
  • Lack of verified benchmarks and client references raises questions about _etched’s technology and capabilities.
  • Continued development and validation of _etched’s technology will determine its impact on the AI chip market.


OpenAI’s reported plans to develop its own AI hardware signal a strategic shift in the company’s approach to AI technology. With its track record of innovation and groundbreaking research, OpenAI’s entry into the AI hardware market holds promise for advancements in AI computing. As OpenAI progresses with its hardware development efforts, the industry awaits eagerly to see the impact of its technology on AI applications.

  • OpenAI’s plans to develop its own AI hardware mark a significant strategic shift for the company.
  • With a history of innovation, OpenAI’s entry into the AI hardware market raises expectations for breakthrough advancements.
  • Progress in OpenAI’s hardware development efforts will shape the future of AI computing and applications.


In conclusion, the landscape of AI chip makers in 2024 is diverse and dynamic, with established players and emerging startups driving innovation and advancement. From industry giants like Nvidia and Intel to startups like SambaNova Systems and Groq, each company brings unique strengths and contributions to the AI hardware ecosystem. As AI continues to permeate various industries and applications, the role of AI chips in powering this transformation cannot be overstated. With ongoing research, development, and collaboration, the future of AI hardware holds immense promise for unlocking new possibilities and driving meaningful impact across domains.

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