AI chip companies are redefining the semiconductor market with innovation in inference and accelerator chips. Industry-leading companies like Nvidia, AMD, and Intel are competing to capture maximum AI market share with innovations in AI hardware. Both leading and emerging companies like Groq Inc and Cerebas AI are introducing new landscapes in Inference chips and AI Accelerators. 

In this blog we have compiled emerging and leading AI chip companies, including the latest chips launched. Check out the list below. 

The emerging startups and top AI companies are listed in this table alphabetically.

Best AI Chip Companies

AMD

AMD (Advanced Micro Devices) is a popular fabless chip manufacturer known for its CPU, GPU, and AI chips. It was founded in 1969 and is headquartered in the USA.

Its AI-focused chips are known to meet high-performance computing (HPC) applications. In October 2024, AMD announced its AI chip called Instinct MI325X. This chip aims to support large-scale generative AI models.

AMD has invested in AI chips to compete with its competitors, Nvidia Corporation, Intel, and upcoming players like Groq. In 2022, AMD acquired Xilinx leading to breakthroughs in embedded AI and edge computing. This acquisition helped combine AMD's CPU and GPU technologies with Xilinx's FPGAs and Adaptive SoCs (System-on-Chip).

Popular AMD AI chips:

• AMD Instinct™ MI300X Accelerator

• AMD Ryzen™ AI 300 Series Processors (Windows PCs with AI built in)

• AMD Versal™ AI Core and Versal AI Edge

• AMD Zynq™ Adaptive SoCs

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On 6th January 2025, AMD announced the expansion of its consumer and commercial AI PCs by AMD Pro Technologies into Ryzen AI Max, AI 300, and AI 200 Series processors. This allows users to have enterprise-grade security and manageability, designed to help secure the modern enterprise and streamline IT operations.

AWS

Amazon has its very own ai chips customized to power (Amazon Web Services) AWS cloud services. Amazon built its own chips to reduce reliance on Nvidia chips and control the costs for AWS AI Services.

Popular Amazon AI chips:

• AWS Inferentia 2

This is the second generation of the AWS Inferentia chip, which is faster and more efficient than its earlier version. It was released in 2023 and has a throughput 4x higher and 10x lower latency than Infernetia 1. With higher efficiency, it is used for large-scale natural language processing (NLP) and computer vision.

• AWS Trainium

It was designed for AI model training that will train AI models at 50% lower costs compared to GPUs. It was released in 2021. It is used in Amazon EC2 Trn1 for large-scale AI training by AWS customers for generative AI applications.  
 

• AWS Inferentia

It was designed for AI Inference for processing trained models in 2019 that is optimized for deep learning AI models at low cost and high efficiency. It is used in AWS SageMaker, Amazon Alexa, and AWS Lambda.

Cerebras AI

Cerebras was founded in 2015 and specializes in wafer-scale chips. Wafer-scale chips offer higher memory bandwidth compared to GPUs, which makes them more preferable for LLMs, generative AI, and HPC workloads. Several organizations in fields like cryptography, energy, and medical research use Cerebras's CS-2 and CS-3 systems to build on-premise supercomputers.

Cerebras AI products:

• Cerebras AI Inference

• Cerebras Cloud

• Cerebras AI Supercomputers

• Cerebras CS-3 System 

• Cerebras WSE chip - fastest AI processor on earth 
   

On 11th March 2025, Cerebras announced the launch of six new AI inference data centers that will be powered by Cerebras Wafer-Scale Engines. Embedded with thousands of Cerebras CS-3 systems, these data centers are expected to serve 40 million Llama 70B tokens per second, making Cerebras the world’s top provider of high-speed inference and the largest domestic high-speed inference cloud.

Cerebras AI Inference Data Centers (Operational)

• Santa Clara, California

• Stockton, California

• Dallas, Texas
   

Cerebras AI Inference Data Centers (Upcoming) 

• Minneapolis, Minnesota (Q2 2025)

• Oklahoma City, Oklahoma (Q3 2025)

• Montreal, Canada (Q3 2025)

• Midwest US (Q4 2025)

• Eastern US (Q4 2025)

• Europe (Q4 2025)
   

Back in 2023, Cerebras and G42 partnered to announce Condor Galaxy, a global network of nine interconnected AI supercomputers. This global network of supercomputers promises to reduce AI model training time. This includes Condor Galaxy 1 (CG-1), CG-2, and CG-3.

Google

Google has developed its own custom AI chips called the TPUs (Tensor Processing Units). These chips are specifically optimised for AI workloads for deep learning models.

Google TPUs are application-specific integrated circuits (ASICs) that can handle matrix-heavy computations required in AI/ML models. 

TPU was announced back in 2016. In 2024, Google launched the latest trillium TPU chips (TPU v6). These are the 6th generation chips of TPU. These chips are used to train Google's AI model, Gemini 2.0. 

On 17th March 2025, Google announced its partnership with Taiwan's Mediatek to develop the next generation of its TPU AI chips. This partnership is driven to develop Google's TPU's at more cost-effective pricing compared to Broadcom which is Google's current TPU partner.

Apart from this AI chip manufacturer partnership, Google will also develop its own AI chips that will be used for internal research and development and will be available to its cloud customers.

Popular Google AI Chips 

• TPU v1 

• TPU v2 

• TPU v3 

• TPU v4 

• TPU v5 

• Trillium chips (TPU v6)
   

Groq Inc

Groq Inc. aims to deliver Fast AI Inference integrated with an LPU (Language Processing Unit). It was founded in 2016 by a group of former Google engineers. Groq's Fast AI Inference aims to outperform GPU's inference speed for large language models. It is headquartered in Mountain View, California.

On 10th February 2025, Groq secured a USD 1.5 billion commitment form Saudi Arabia to deliver advanced AI chips. This investment is set to be used by Groq to expand its existing data center in Dammam, Saudi Arabia.

Popular Groq AI chips:

• GroqChip™

• Groq LPU™

• GroqRack Compute Cluster - AI chips made up of interconnected chips.

• GroqCloud
   

Back in 2022, Groq acquired Dataflow Systems Pioneer Maxeler Technologies. This acquisition was driven to develop next era computing for artificial intelligence (AI), machine learning (ML), and high-performance computing (HPC) solutions.

Intel

Intel is one of the leading players in the CPU market, and it is known for its semiconductor chips and design. Apart from design, it is known for its foundry and for building chips, which makes it different from its competitors, NVIDIA and AMD.

It was founded in 1968 and is headquartered in Santa Clara, California, USA. In 2024, Intel launched its new AI chip called the Gaudi 3 accelerator. These chips are designed and built for training and interference to increase memory for LLM efficiency and cost-effectiveness. 

Popular Intel AI chips:

• Intel Gaudi AI Accelerators : Gaudi 3 & Gaudi 2
  Intel’s Gaudi Series offers AI model training and Inference. The Gaudi 2 launched in 2022 was designed for deep learning & inference, built with HBM (High Bandwidth Memory) architecture for faster data access. The Gaudi 3, launched in 2024, is designed for training and inference to increase memory for LLM efficiency. 

• Intel Core Ultra Processors: AI-powered PCs that integrate AI and CPU architecture, thus enhancing performance and improving AI tasks efficiently.
   

Meta

Meta is known for developing custom AI chips. In 2024, Meta launched MTIA v2 (Meta Training and Inference Accelerator). These are next-gen custom AI chips built to handle large-scale models efficiently. These chips improve compute performance, memory bandwidth, and energy efficiency compared to MTIA v1. These enable better AI inference for Meta’s platforms, like Facebook and Instagram.

On 11th March 2025, Meta tested its first in-house chip for training AI systems. This new training chip is a dedicated accelerator designed for AI-specific tasks. This can make it power-efficient compared to other integrated graphics processing units (GPUs) generally used for AI workloads.

Popular Meta AI chips:

• MTIA v1: It was designed to optimize Meta-specific AI workloads. It operates at 800 MHz and is Meta’s first in-house AI inference accelerator. It features a grid of 64 processing elements based on dual RISC-V cores. It was launched in 2023.

• MTIA v2: Launched in 2024, it is fabricated using a 5 nm chip with an 8*8 grid of processing elements on-chip SRAM. It operates at 1.35 GHz with a TDP (Thermal Design Power) of 90 watts. It was built upon the MTIA v1 (Meta Training Inference and Accelerator).   
   

Microsoft

Microsoft is known for its software solutions, apart from which it also delivers its clients AI chips such as Maia 100 a part of the Azure Maia AI Accelerator series. This is the first custom AI accelerator designed by Microsoft.

Popular Microsoft AI chips:

• Azure Maia AI Accelerator - designed to enhance the AI training & Inference.

• Azure Cobalt CPU- designed on ARM-Based processor for general cloud computing in Microsoft Azure.

Azure Maia is Microsoft's first in-house AI accelerator that was designed to optimize AI workloads, whereas, Azure Cobalt CPU is an arm-based processor to reduce workloads in Microsoft Cloud.

NVIDIA

Founded in 1993, it is one of the top companies in AI chip development, especially for deep learning and large-scale AI model training chips. It is headquartered in Santa Clara, California, USA.

Apart from traditional GPUs (Graphic Processing Units), their AI chips are ideal for training LLMs (Large Language Models). Its famous products, such as H100 (Hopper) and A100 (Ampere), are some of the widely used AI chips.

Nvidia AI chips are in high demand after its big GPUs for OpenAI's ChatGPT launched in late 2022. Several cloud companies use its high-performance chips, including Microsoft, Google, and Amazon.

Recently, on 18th March 20225, NVIDIA launched its new AI chips, the Blackwell Ultra and Vera Rubin series. The Blackwell ultra chips are scheduled to be shipped for use this year, and the Vera Rubin series, a next-generation GPU, is expected to be shipped in 2026. The Vera Rubin will be named after physicist Richard Feynman and will be available for use by 2028. These chips help build and run real-time generative AI at 25 times less cost and energy. 

Popular NVIDIA AI Chips: 

• H100 Tensor Core GPU

• A100 TENSORE Core GPU

• V100 Tensor Core GPU

• Blackwell Ultra (New Launch) 

• Vera Rubin (New Launch)
   

Qualcomm

Qualcomm is known for producing wireless technology related to semiconductors and software.

Founded in 1985, its specializes in mobile communication technologies such as 5G, 4G, CDMA2000, TD-SCDMA, and WCDMA. Its AI capabilities are embedded in the Snapdragon® series and dedicated Hexagon™ AI processors. These are designed for AI tasks like computer vision, NLP, and generative AI.

Popular Qualcomm AI Chips:

• Snapdragon 8 Gen 3 — AI Engine for smartphones

• Qualcomm AI Engine / Hexagon NPU 

• Qualcomm Cloud AI 100 — An AI inference accelerator for data centers
   

On 6th January 2025, it launched its new AI chips that will power 600+ AI-enabled personal computers in 2025. In order to boost its AI capabilities and improve performance and the overall life of Windows laptops, QUALCOMM introduced the Snapdragon X Elite and X Plus processors.

This move is driven by its desire to compete and position itself with its competitor Intel in the AI PCs segment.

SambaNova Systems

SambaNova Systems is known to develop high-performance, high-precision hardware and software systems. These are used for high-volume generative AI workloads. It has built an AI platform, "SambaNova Suite", which can be used to build your own model. SambaNova's Reconfigurable Dataflow Architecture (RDA) allows parallel processing for large-scale AI models.

It was founded in 2017 in Palo Alto, California. On 7th March 2025, SambaNova announced the expansion of its SambaNova Cloud deployment and its partnership with SoftBank Corp in Japan. This collaboration aims to add SambaNova's efficient AI chip racks to its new AI data center in Japan. This collaboration aims to meet the growing demand for AI through higher efficiency and lower costs to run LLMs in sectors like finance, telecom, and healthcare.

Popular SambaNova AI Chips:

• SN40L System — Designed for generative AI and LLMs

• DataScale® Platform 

• SambaNova Suite™
   

Other Notable AI Chip Companies

• Apple

• Huwaei 

• IBM

• Tenstorrent
   

Conclusion

The AI chip industry is evolving rapidly, with both emerging and leading startups pushing the boundaries of AI hardware (AI chip design and chip architecture) and AI solutions (AI operations). From powerful AI accelerators to efficient AI training and inference chips, these companies are driving the wave of AI advancements.

With expansions on the horizon in AI chip innovation, investors looking at AI chip companies to invest in are offered a range of opportunities. As demand for AI-powered solutions rises, the demand for new innovation and advancements in AI technology continues to grow, ensuring a future in technology.

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