Top 20 AI Chip Makers: NVIDIA & Its Competitors in 2025
Based on our experience running AIMultiple’s cloud GPU benchmark with 10 different GPU models in 4 different scenarios, these are the top AI hardware companies for data center workloads. Follow the links to see our rationale behind each selection:
Datacenter Chip Makers
These chip makers focus on datacenter chips:
- Leading producer
- Leading producer
- Leading producer
- Public cloud & chip producer
- Public cloud & chip producer
- NVIDIA
- Leading producer
- Blackwell Ultra
- AMD
- Leading producer
- MI400
- Intel
- Leading producer
- Gaudi 3A
- AWS
- Public cloud & chip producer
- Trainium3
- Alphabet
- Public cloud & chip producer
- Ironwood
- Alibaba
- Public cloud & chip producer
- ACCEL
- IBM
- Public cloud & chip producer
- NorthPole
- Huawei
- Public cloud & chip producer
- Ascend 910
- CGroq
- Public AI cloud & chip producer
- LPU Inference Engine
- SambaNova Systems
- Public AI cloud & chip producer
- SN40LM
- Microsoft Azure
- Public AI cloud & chip producer
- Maia 100
- Untether AI
- Public AI chip producer
- speedAI240
- Cerebras
- AI startup
- WFE-3d-Matrix
- Rebellions
- AI startup
- Rebel
- Tenstorrent
- AI startup
- Wormhole_etched
- Sohu
- Extropic
- AI startup
- Apple
- Upcoming producer
- M4Meta
- Upcoming producer
- MTIA v2
- OpenAI
- Upcoming producer
- TBDGraphcore
- Other producers
- Bow IPU
- Myhtic
- Other producers
- M2000
Sorting and Market Share
Sorting is by category. Vendors are ranked by estimated market share within top 3 categories (i.e. leading producer, public cloud, public AI cloud) because sales numbers or cloud usage can be estimated. Vendors in the last 3 categories (i.e. AI startup, upcoming producer, other producers) are sorted alphabetically.
| Vendor | Products |
|---|---|
| Apple | A18 Pro, A18 iPhone 16 Pro, iPhone 16 |
| Huawei | Kirin 9000S Huawei Mate 60 series |
| MediaTek | Dimensity 9400, Dimensity 9300 Plus Oppo Find X8, Vivo X200 series, Samsung Galaxy Tab S10 Plus, Tab S10 Ultra |
| Qualcomm | Snapdragon 8 Elite (Gen 4), Snapdragon 8 Gen 3 Samsung Galaxy S25 Ultra, Xiaomi 14, OnePlus 12, Samsung Galaxy S24 series |
| Samsung | Exynos 2400, Exynos 2400e Exynos 2400, Exynos 2400e |
Edge AI Chips
The demand for low-latency processing has driven innovation in edge AI chips. These chips’ processors are designed to perform AI computations locally on devices rather than relying on cloud-based solutions:
- NVIDIA Jetson Orin 202227510-60W Robotics, Autonomous Systems
*These are the maximum quoted values by the vendors. TOPS is tera operations per second.
NVIDIA's Dominance
NVIDIA has been designing graphics processing units (GPUs) for the gaming sector since the 1990s. NVIDIA is a fabless chip manufacturer that outsources most of its chip manufacturing to TSMC. Its main businesses include:
- The PlayStation 3 and Xbox both use NVIDIA graphics arrays.
- NVIDIA’s GPUs for retail users include the GeForce series.
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.
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.
NVIDIA Dynamo, announced at GTC 2025, is a new open-source inference framework designed for high-throughput, low-latency deployment of generative AI models in distributed environments, boosting request serving by up to 30x on NVIDIA Blackwell.
Release of DeepSeek’s R1 showed that state of the art models could be trained with a relatively small number of GPUs. While NVIDIA dominates the AI “training” market, competition is heating up in “inference” – the deployment of AI models for real-world tasks.
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. Various startups, research institutes, enterprises, and tech giants have adopted AMD hardware in 2023.
