Top 20 AI Chip Makers: NVIDIA & Its Competitors in 2025
Which are the leading AI chip producers?
1. NVIDIA
NVIDIA has been designing graphics processing units (GPUs) for the gaming sector since 1990s. NVIDIA is a fabless chip manufacturer that outsources most of its chip manufacturing to TSMC. Its main businesses include:
GPUs for graphics
The PlayStation 3 and Xbox both use NVIDIA graphics arrays.
NVIDIA’s GPUs for retail users include GeForce series.
Desktop AI solutions
Project Digits is a desktop AI solution for AI engineers and data scientists that is planned to:
Cost $3k
Be about the same size as a Mac mini and it comes with 128GB memory
Handle LLM inference and finetuning for models up to 200 billion parameters.
Datacenter solutions
The company makes AI chips following its Ampere, Hopper and most recently Blackwell architectures. Thanks to the generative AI boom, NVIDIA had excellent results in the past years, reached a trillion in valuation and solidified its status as the leader of GPU and AI hardware markets.
NVIDIA’s chipsets are designed to solve business problems in various industries. DGX™ A100 and H100 have been successful flagship AI chips of Nvidia, designed for AI training and inference in data centers.2 NVIDIA followed up on these with
H200, B300 and GB300 chips
HGX servers such as HGX H200 and HGX B300 that combine 8 of these chips
NVL series and GB200 SuperPod that combine even more chips into large clusters.3
Cloud GPUs
Thanks to the strength of its datacenter offering, NVIDIA almost has a monopoly on the cloud AI market with most cloud players offering only NVIDIA GPUs as cloud GPUs.
NVIDIA also launched its DGX Cloud offering providing cloud GPU infrastructure directly to enterprises bypassing cloud providers.
Recent developments
Release of DeepSeek’s R1 showed that state of the art models could be trained with a relatively small number of GPUs. This led to a reduction in NVIDIA’s stock price. Though this is not investment advice, this can be positive for NVIDIA since the more utility computing power provides, the more widely it should be used (i.e. Jevons paradox4 ).
However, given that GPU systems’ performance is improved multiple times annually thanks to improvements in chip design and interconnect, buyers would be wise to not buy beyond their annual needs since this can lead to owning obsolete or underperforming systems.
2. AMD
AMD is a fabless chip manufacturer with CPU, GPU and AI accelerator products.
AMD launched MI300 for AI training workloads in June 2023 and is competing with NVIDIA for market share. There are startups, research institutes, enterprises and tech giants that adopted AMD hardware in 2023 since Nvidia AI hardware has been difficult to procure due to rapidly increasing demand with the rise of generative AI triggered by the launch of ChatGPT.5 6 7 8
AMD will be releasing MI350 series to replace MI300 and compete with NVIDIA’s H200. AMD claims that MI325X, another recent chip, has market leading inference performance.9
AMD is also working with machine learning companies like Hugging Face to enable data scientists to use their hardware more efficiently.10
The software ecosystem is critical as hardware performance relies heavily on software optimization. For example, AMD and NVIDIA had a public disagreement over benchmarking H100 and MI300. The focus of the disagreement was the package and floating point to use in the benchmark. According to the latest benchmarks, it appears that MI300 is better or on par with H100 for inferencing on a 70B LLM.11 12
Software
While AMD hardware is catching up to NVIDIA, its software lags behind in terms of usability. While CUDA works out of the box for most tasks, AMD software requires significant configuration.13
Ecosystem
Like NVIDIA, AMD is selectively investing into users of its solutions to drive adoption of its hardware.14
3. Intel
Intel is the largest player in the CPU market and has a long history of semiconductor development. Unlike NVIDIA and AMD, Intel uses its own foundry to build its chips.
Gaudi3 is the latest AI accelerator processor from Intel. 15 However, Intel’s sales guidance for Gaudi3 was ~$500M for 2024 which is significantly lower compared to the billions that AMD is projecting to earn in 2024.
Intel is experiencing governance issues as shown by its CEO Pat Gelsinger’s departure in December 2024. A significant share of Intel’s board members lack experience in leading a semiconductor company in an operational manner.16 After the departure of its CEO, Intel’s strategy in the AI and foundry markets is not yet clear.
Which public cloud providers produce AI chips?
4. AWS
AWS produces Tranium chips for model training and Inferentia chips for inference. Though AWS is the market leader in public cloud, it started building its own chips after Google.
5. Google Cloud Platform
Google Cloud TPU is the purpose-built machine learning accelerator chip that powers Google products like Translate, Photos, Search, Assistant, and Gmail. It can be used via the Google Cloud as well. Google announced TPUs in 2016.17 Latest TPU is Trillium, the 6th generation TPU.18
Edge TPU, another accelerator chip from Google Alphabet, is smaller than a one-cent coin and is designed for edge devices such as smartphones, tablets, and IoT devices.
6. Alibaba
Alibaba produces chips like Hanguang 800 for inference. However, some North American, European and Australian organizations (e.g. those in the defense industry) may not prefer to use Alibaba Cloud for geopolitical reasons.
7. IBM
IBM announced its latest deep learning chip, artificial intelligence unit (AIU), in 2022.19 . IBM is considering using these chips to power its watson.x generative AI platform.20
AIU builds on “IBM Telum Processor” which powers AI processing capabilities of IBM Z mainframe servers. At launch, Telum processors’ highlighted use cases included fraud detection.21
IBM also demonstrated that merging compute and memory can lead to efficiencies. These were demonstrated in the NorthPole processor prototype.22
8. Huawei
Huawei’s HiSilicon Ascend 910C is part of the Ascend 910 family of chips introduced in 2019.
Due to sanctions, AI labs in China can not buy the newest highest performance chips from US firms like NVIDIA or AMD. Therefore, they are experimenting with Ascend 910C.
Huawei’s cloud is hosting DeepSeek models and a researcher at DeepSeek claims that it can reach 60% of NVIDIA H100 inference performance. 23 24
Which cloud AI providers produce their own chips?
These providers do not have public clouds with comprehensive capabilities like the hyperscalers. They provide limited cloud services typically focused on AI inference. We were able to sign up to these services without talking to sales teams:
8. Groq
Groq has been founded by former Google employees. The company represents LPUs, a new model for AI chip architecture, that aims to make it easier for companies to adopt their systems. The startup has already raised around $350 million and produced its first models such as GroqChip™ Processor, GroqCard™ Accelerator, etc.
The company is focused on LLM inference and released benchmarks for Llama-2 70B.25
In Q1 2024, the company shared that 70k developers signed up on its cloud platform and built 19k new applications.26
On March 1, 2022, Groq had acquired Maxeler, which has high performance computing (HPC) solutions for financial services.27
9. SambaNova Systems
SambaNova Systems was founded in 2017 with the goal of developing high-performance, high-precision hardware-software systems for high volume generative AI workloads. The company has developed the SN40L chip and raised more than $1.1 billion in funding.28 29
It is important to note that SambaNova Systems also leases its platform to businesses.30 AI platform as service approach of SambaNova Systems makes their systems easier to adopt and encourages hardware reuse for circular economy.
Which are the leading AI chip startups?
We would also like to introduce some startups in the AI chip industry whose names we may hear more often in the near future. Even though these companies were founded only recently, they have already raised millions of dollars.
10. Cerebras
Cerebras was founded in 2015 and is the only major chip maker focusing on wafer-scale chips. 31 Wafer scale chips have advantages in parallelism compared to GPUs thanks to their higher memory bandwidth. However, designing and manufacturing such chips is an emerging technology.
Cerebras chips’ include:
WSE-1 with 1.2 trillion transistors and 400k processing cores.
WSE-2 with 2.6 trillion transistors and 850k cores, announced in April 2021. It leveraged TSMC’s 7nm process
WSE-3 with 4 trillion transistors and 900k AI cores, announced in March 2024. It leverages TSMC’s 5nm process32
Celebra’s system works with pharmaceutical companies such as AstraZeneca and GlaxoSmithKline and research labs that rely on it for simulations. It also targets LLM makers since its chips can lower inference costs for frontier models.
Cerebras also offers its chips on its cloud to enterprises.
11. d-Matrix
d-Matrix follows a novel approach ditching the traditional von Neumann architecture in favor of in-memory compute. While this approach has the potential to resolve the bottleneck between memory and compute, it is a new and yet unproven approach.33
12. Rebellions
Korea based startup raised $124M in 2024 and is focused on LLM inference.34
Rebellions merged with another Korean semiconductor design firm, SAPEON and reached a unicorn valuation in 2024.35
13. Tenstorrent
Tenstorrent produces the Wormhole chip, desktop machines for researchers and servers (e.g. Tenstorrent Galaxy) powered by Wormhole chips. The company also provides the software stack for its solution.
Tenstorrent raised $700M at a valuation of more than $2.6 billion from investors including Jeff Bezos in December 2024.36
14. _etched
Their approach sacrifices flexibility for efficiency by burning the transformer architecture into their chips.
The team claims
To have built the world’s first transformer ASIC, Sohu.
That 8 Sohu chips can generate >500,000 tokens/second. This is an order of magnitude more than what 8 NVIDIA B200s can achieve.
Currently, these are based on team’s internal measurements. AIMultiple team has not yet come across any benchmarks or client references. We are curious about:
What happens when the model becomes outdated? Do users need to buy a new chip or can the old chip be reconfigured with the next model?
How they ran their benchmark? Which quantization and model were used?
We’ll be updating this as soon as the _etched team releases more details. It will be interesting to see whether burning model to chips will be sustainable given the release of new models every few months.
What are upcoming AI hardware producers?
Though these are compelling AI hardware solutions, there are currently limited benchmarks on their effectiveness since they are newcomers to the market.
15. Apple
Apple’s project ACDC is reported to be focused on building chips for AI inference.37 Apple is already a major chip designer with its internally designed semiconductors used in iPhone, iPads and Macbooks.
16. Meta
Meta Training and Inference Accelerator (MTIA) is a family of processors for AI workloads such as training Meta’s LLaMa models.
The latest model is Next Gen MTIA which is based on TSMC 5nm technology and is claimed to have 3x improved performance vs. MTIA v1. MTIA will be hosted in racks containing up to 72 accelerators.38
MTIA is currently for Meta’s internal usage. However, in the future, if Meta launched a LLaMa based enterprise generative AI offering, these chips could power such an offering.
17. Microsoft Azure
Microsoft launched Maia AI Accelerator in November 202339
18. OpenAI
OpenAI is finalizing the design of its first AI chip with Broadcom and TSMC using TSMC’s 3-nanometer technology. OpenAI’s chip team’s leadership has experience with designing TPUs at Google and they aim to have their chip mass produced in 2026.40
What are other AI chip producers?
19. Graphcore
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.
The company’s long term viability was at risk as it was losing ~$200M per year.41 It got acquired by Softbank for $600m+ in October 2024.42
20. Mythic
Mythic was founded in 2012 and is focused on edge AI. Mythic follows an unconventional path, an analog compute architecture, that aims to deliver power-efficient edge AI computing.
It developed products such as M1076 AMP, MM1076 key card, etc., and has already raised about $165 million in funding.43
Mythic laid of most of its staff and restructured its business with its funding round in March 2023.44
What are the AI chip makers in China?
Since the US sanctions prevented many Chinese companies from acquiring the most advanced AI chips from AMD and NVIDIA, Chinese buyers have increased their purchases from local producers.
Other than Huawei and Alibaba covered above, these are the leading AI chip producers in China:
Cambricon focuses on AI hardware and expects ~$150M in sales in its latest year of operations.45
Baidu is using Kunlun chips in its cloud and is designing the 3rd generation chip. Kunlun 2 was comparable to NVIDIA A100.
Biren, founded by NVIDIA alumni, produces BR106 & BR110 GPU chips.
Moore Threads produces MTT S2000 GPUs.