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10 top AI hardware and chip-making companies in 2025

Due to rapid AI hardware advancement, companies release advanced products yearly to keep up with the competition. The new competitive product on the market is the AI chip.

With a surge in popularity and advancement, AI hardware has become a competitive market. AI hardware companies must have quick turnarounds in product advancements to have the newest and most effective products on the market.

Although these 10 AI hardware companies focus on CPUs and data center technologies, their specializations have slowly broadened as the market expands. Now, these companies are competing to create the most powerful and efficient AI chip on the market.

10 top companies in the AI hardware market

The following AI hardware and chip-making companies are listed in alphabetical order.

Alphabet

Alphabet, Google's parent company, has various products for mobile devices, data storage and cloud infrastructure. Cloud Tensor Processing Unit (TPU) v5p is purpose-built to train large language models and generative AI. Each TPU v5p pod has 8,960 chips, and each chip has a bandwidth of 4,800 Gbps.

TPU v6e is the newest Trillium chip, released in October 2024. This chip is similar to TPU v5e, with a pod size of 256 chips and four ports per chip. However, the TPU v6e chip has a peak compute performance per chip that is 4.7 times higher than that of TPU v5e.

Alphabet has focused on producing powerful AI chips to meet the demand for large-scale projects. In December 2024, Alphabet released a new quantum computing chip, Willow. With 105 qubits and the ability to scale up, the Willow chip reduces error in quantum computing faster and more accurately than its predecessors.

AMD

AMD released its latest CPU microarchitecture chip design, Zen 5, and its next generation of Ryzen processors, Ryzen 9000 Series, in the latter half of 2024.

AMD's new Instinct MI300 Series chip, MI325X, was also released in 2024. This upgrade from MI300X has a larger bandwidth at 6 TBps. An improved chip version, MI355X, is expected to be released in 2025. These AI GPU accelerators are meant to rival Nvidia's Blackwell B100 and B200.

Apple

Apple Neural Engine, specialized cores based on Apple chips, has furthered the company's AI hardware design and performance. Neural Engine led to the M1 chip for MacBooks. Compared to the generation before, MacBooks with an M1 chip are 3.5 times faster in general performance and five times faster with graphic performance.

After the success of the M1 chip, Apple announced further generations. As of 2024, Apple has released the M4 chip. The M4 chip has a Neural Engine that is three times faster than the M1 chip and 1.5 times faster than M2. The M5 chip is expected to be released in the first half of 2025.

Apple and Broadcom are developing an AI-specific server chip, Baltra. This chip is expected to be released in 2026 but will only be used internally by the companies.

AWS

AWS has switched its focus from cloud infrastructure to chips. Its Elastic Compute Cloud (EC2) Trn1 instances are purpose-built for deep learning and large-scale generative models. They use AWS Trainium AI accelerator chips to function.

The trn1.2xlarge instance was the first iteration. It only had one Trainium accelerator, 32 GB of instance memory and 12.5 Gbps network bandwidth. Now, AWS has the EC2 Trn2 instance and UltraServer. A Trn2 instance has 16 Trainium2 chips, 1.5 TB of accelerator memory and 46 TBps bandwidth. The Trn2 UltraServer has 64 Trainium2 chips over four Trn2 instances, 6 TB of shared accelerator memory and 185 TBps bandwidth.

AWS Inferentia is a machine learning chip that generates high-performance inference predictions at a low cost. Trainium accelerators train models, while Inferentia accelerators deploy models.

Cerebras Systems

Cerebras is making a name for itself with the release of its third-generation wafer-scale engine, WSE-3. WSE-3 is deemed the fastest processor on Earth with 900,000 AI cores on one unit. Every core has access to 21 petabytes per second of memory bandwidth.

Compared to Nvidia's H100 chip, WSE-3 has 7,000 times larger bandwidth, 880 times more on-chip memory and 52 times more cores. This WSE-3 chip is also 57 times larger in area, so more space is necessary to house the chip in a server.

IBM

Telum was IBM's first specialized AI chip, and Telum II will be released in 2025. IBM has also set out to design a powerful successor to rival its competitors.

In 2022, IBM created Artificial Intelligence Unit. The AI chip is purpose-built and runs better than the average general-purpose CPU. Made from similar architecture, IBM will release Spyre Accelerator in 2025. Spyre contains 25.6 billion transistors over 14 miles of wire.

IBM is working on the NorthPole AI chip, which does not have a release date. NorthPole differs from IBM's TrueNorth chip. The NorthPole architecture is structured to improve energy use, decrease the amount of space the chip takes up and provide lower latency. The NorthPole chip is set to mark a new era of energy-efficient chips.

Intel

Intel has made a name for itself in the CPU market with its AI products.

Xeon 6 processors launched in 2024 and have been shipped to data centers. These processors offer up to 288 cores per socket, enabling faster processing time and enhancing the ability to perform multiple tasks at once.

Intel has released the Gaudi 3 GPU chip, in competition with Nvidia's H100 GPU chip. The Gaudi 3 chip trains models 1.5 times faster, output results are 1.5 times faster and it uses less power than Nvidia's H100 chip. Intel has canceled the launch of the Falcon Shores AI GPU chip. The Jaguar Shores chip is still set to launch in 2026 as the successor of the Gaudi 3 chips. However, the company is focusing away from standalone AI accelerators.

Nvidia

Nvidia became a strong competitor in the AI hardware market when its valuation surpassed $1 trillion in early 2023. The company's current work includes its B100 chip and Blackwell GPU microarchitecture. Both products are critical technologies for resource-intensive models. Nvidia also offers AI-powered hardware for the gaming sector.

The Blackwell GPU microarchitecture is replacing the Grace Hopper platform. Blackwell is 2.5 times faster and 25 times more energy-efficient than its predecessors. The Blackwell microarchitecture is designed to increase efficiency with scientific computing, quantum computing, AI and data analytics.

The Blackwell B200 GPU AI chip has been delayed and should be released in 2025. Nvidia also plans to launch a new accelerator, Rubin, in late 2025 or early 2026.

Qualcomm

Although Qualcomm is relatively new in the AI hardware market compared to its counterparts, its experience in the telecom and mobile sectors makes it a promising competitor.

Qualcomm's Cloud AI 100 chip beat Nvidia H100 in a series of tests. One test was to see the number of data center server queries each chip could carry out per watt. Qualcomm's Cloud AI 100 chip totaled 227 server queries per watt, while Nvidia H100 hit 108. The Cloud AI 100 chip also managed to net 3.8 queries per watt compared to Nvidia H100's 2.4 queries during object detection.

In 2024, the company released Snapdragon 8s Gen 3, a mobile chip. This chip supports 30 AI models and has generative AI features, like image generation and voice assistants. In 2025, Qualcomm released the newest version, Snapdragon 8 Elite, which improves AI performance by 45%.

Tenstorrent

Tenstorrent builds computers for AI and is led by the same man who designed AMD's Zen chip architecture, Jim Keller. Tenstorrent has multiple hardware products, including its Wormhole processors and Galaxy servers, which create Galaxy Wormhole Server.

Wormhole n150 and n300 are Tenstorrent's scalable GPUs. N300 nearly doubles every spec of n150. These chips are for network AI and are put into Galaxy modules and servers. Each server holds up to 32 Wormhole processors, 2,560 cores and 384 GB of GDDR6 memory.

Editor's note: This article was updated in February 2025 to update the AI chips each company has to offer.

Devin Partida is editor in chief of ReHack.com and a freelance writer. She has knowledge of niches such as biztech, medtech, fintech, IoT and cybersecurity.

Kelly Richardson is site editor for Informa TechTarget's Search Data Center site.

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