NVIDIA dominates the discrete GPU market for desktops, holding nearly 88% market share in 2024, primarily through its GeForce product line, which is popular among gamers and professional users alike.
It is also the market leader in AI and machine learning hardware, with nearly 98% market share for GPUs and AI processors used in data centers. According to TechInsights, a semiconductor analysis firm, Nvidia shipped 3.76 million data-center GPUs in 2023, up from 2.64 million in 2022. [1]
This surge contributed to NVIDIA’s revenue reaching $96.3 billion for the fiscal year ending in July 2024, a staggering 194.6% year-over-year increase. Approximately 78% of this revenue came from its data center business, with 17.1% from its desktop GPU segment.
While Nvidia’s revenue is growing rapidly, it is also facing strong competition in several key markets. Here, we highlight the top competitors challenging NVIDIA’s dominance in areas like AI chips, data centers, gaming GPUs, and autonomous driving technologies.
Did you know?
NVIDIA’s most expensive acquisition to date was the purchase of Mellanox Technologies for $6.9 billion in 2020. This acquisition helped Nvidia strengthen its position in the data center market by offering end-to-end computing solutions combining GPUs and networking. [2]
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14. IBM
Annual Revenue: $62.36 billion
Competition: AI and HPC
IBM competes with NVIDIA primarily in the areas of AI, high-performance computing (HPC), and data center solutions. More specifically, its Watson AI and Power Systems compete against NVIDIA’s AI-accelerated GPUs used for machine learning workloads.
IBM has a strong presence in hybrid cloud services — it provides AI and automation solutions tailored for various industries like finance, healthcare, and logistics. Its consulting division helps companies deeply integrate AI and cloud technologies, offering a more comprehensive approach than Nvidia’s hardware-focused strategy.
According to a market research report, the global IBM Watson services was valued at $5.5 billion in 2023 and is projected to reach $76.47 billion by 2033, expanding at a CAGR of 30.1%. [3]
IBM is also heavily investing in quantum computing. In 2023, it unveiled the powerful Condor processor featuring 1,121 superconducting qubits, which may compete with NVIDIA’s future ventures in this domain. [4]
13. Marvell Technology
Annual Revenue: $5.5 billion
Competition: Data infrastructure
Marvell Technology is a semiconductor company that competes with NVIDIA in certain segments, particularly in the data center, cloud, and 5G infrastructure markets. While NVIDIA focuses on GPUs, Marvell focuses on networking, storage, and processor technologies for data centers.
Marvell develops Ethernet, data center switches, data processing units, and storage controllers, which it sells to OEMs, cloud service providers, and telecommunications companies. It has a strong footprint in the 5G infrastructure market — it works with major telecom providers and offers products specifically designed to optimize 5G base stations.
Marvell holds a total of 16,347 patents worldwide, covering semiconductor, networking, and 5G technologies. Of these, 62% are currently active. The majority of the patents have been filed in the US, followed by China and European countries. [5]
12. Synopsys
Annual Revenue: $5.84 billion
Competition: Provides AI-driven design automation tools
Synopsys provides software and intellectual property (IP) for chip design, verification, and testing, which semiconductor companies like Nvidia use for building complex GPUs, AI accelerators, and SoCs (system-on-chip). While Synopsys focuses on enabling the design process, Nvidia focuses on building and marketing the chips themselves.
Synopsys also offers solutions in software security and hardware verification. Both Synopsys and Nvidia intersect in ensuring the security of AI-driven systems, though the former focuses more on the verification and testing phase.
Synopsys has witnessed consistent growth in electronic design automation (EDA) tools and semiconductor IP licensing, with 13-15% annual revenue growth. This growth is driven by increasing demand for AI and custom chip designs. [6]
11. Amazon (AWS Inferentia and Trainium Chips)
Annual Revenue: $105.2 billion (from AWS)
Competition: Graviton and Inferentia processors
AWS has developed its own custom silicon, including Graviton processors for general compute workloads and Inferentia chips optimized for machine learning inference. With these two chips, Amazon aims to reduce its dependency on third-party hardware. [7]
Amazon’s Inferentia processors target Nvidia’s A100 and H100 processors, which are designed for machine learning inference workloads. It has also introduced Trainium chips for deep learning training of models with over 100 billion parameters. [8][9]
Plus, Amazon offers AI and machine learning services, such as SageMaker (a fully managed machine learning service) through AWS. In contrast, Nvidia powers AI systems with its GPUs and software platforms like CUDA.
The company has also made significant progress in autonomous systems by acquiring Zoox, an autonomous driving startup. This acquisition boosted Amazon’s position in autonomous driving technology patents and helped the company enhance automation in its distribution network, particularly for last-mile delivery.
10. Alibaba Cloud
Hanguang 800 NPU
Annual Revenue: $14.73 billion
Competition: AI hardware infrastructure
Alibaba Cloud is one of the largest cloud service providers, holding nearly 4% share of the global cloud services market. It offers IaaS, PaaS, and AI services. Like AWS, Alibaba Cloud uses Nvidia GPUs for AI and ML workloads but has also developed its own chips to compete with Nvidia’s hardware.
For example, its Hanguang 800 (AI inference chip) competes with Nvidia’s A100 and H100 GPUs for AI inference in the cloud. Optimized for low energy consumption, Hanguang 800 can complete tasks like AI-driven image analysis (which would typically take one hour) in just five minutes. [10]
In 2022, Alibaba Cloud introduced its chip development platform, Wujian 600. It can help manufacturers design high-performance SoCs for edge-AI computing, leveraging the RISC-V instruction-set architecture in a cost-effective and efficient manner. [11]
In 2023, Alibaba’s annual R&D spending reached $7.6 billion, focusing on AI chips, cloud infrastructure, and custom chip development. For fiscal 2024, Alibaba Cloud Intelligence Group reported annual revenue of $14.73 billion, a 3% year-on-year increase, with EBITA reaching $848 million.
9. Google (Tensor Processing Units – TPUs)
Annual Revenue: $36 billion+ (from Google Cloud)
Competition: Tensor Processing Units (TPUs)
Google has developed Tensor Processing Units (TPUs) to accelerate machine learning workloads. TPUs are optimized for TensorFlow, Google’s open-source machine learning framework that integrates with NVIDIA’s CUDA.
TPUs provide high efficiency for AI model training. For instance, Google reported that TPUs could train models like ResNet-50 up to 15x faster than conventional GPUs (such as Nvidia P100) when introduced.
In 2024, Google announced the sixth generation of its TPU, which delivers 4.7 times more peak compute per chip and is over 67% more energy-efficient than the previous generation. This new TPU is designed to accelerate the next wave of AI models, offering faster, more efficient performance with reduced latency. [12]
Google also leads in AI research and innovation, with pioneering developments in deep learning, natural language processing, cloud computing and, custom hardware for AI. For the fiscal year ending in June 2024, the company spent $47.13 billion in R&D, a 10.65% increase year over year. [13]
8. Huawei
Annual Revenue: $99.37 billion
Competition: AI and 5G infrastructure
Huawei develops its own AI chips, most notably the Ascend processors. The Ascend 910 and Ascend 310 chips, for example, are designed for high-performance AI tasks. The second-generation Ascend 910B series chips have increased maximum theoretical performance by 80 TFLOPS (FP16) compared to its first-generation Ascend 910 series chips. [14]
These chips power Huawei Cloud Services, the second-largest cloud vendor in mainland China. NVIDIA competes here by supplying GPUs to other major cloud providers and enterprises for AI and ML workloads.
Huawei is also the leader in 5G telecommunications and edge computing, holding nearly 30% of the global telecommunications equipment market. Its AI and 5G technologies overlap with NVIDIA’s ambitions to dominate AI at the edge, particularly for autonomous systems and IoT. [15]
7. Micron Technology
Annual Revenue: $21.37 billion
Competition: Memory solutions for AI workloads
Micron develops DRAM, NAND flash memory, and SSD storage solutions. While the company doesn’t directly compete in GPU or AI hardware, its products are crucial for the performance of AI models and cloud infrastructures, where NVIDIA also operates.
Micron’s memory solutions store and manage massive datasets (that are processed by AI chips), providing high-speed access required by AI algorithms. The company is the key supplier of GDDR6X memory, which is critical for NVIDIA’s high-performance GPUs used in AI and data center applications.
Micron is the third-largest producer of DRAM chips, holding approximately 21.5% of the global DRAM market, behind Samsung and SK Hynix. It also holds 9.9% of the NAND flash memory market. [16]
6. Cisco Systems
Annual Revenue: $53.80 billion
Competition: AI, data center infrastructure, and networking
Cisco’s core strength lies in hardware (such as switches and routers) and software solutions for data centers. It also provides cloud networking and software-defined networking (SDN) tools, accounting for nearly 40% of the enterprise network infrastructure market. [17]
Its high-end Nexus switches, especially those designed for data centers, deliver high bandwidth and low-latency networking, which are essential for AI and cloud workloads. Cisco’s ACI architecture supports network automation and workload optimization, focusing on the high-performance computing (HPC) market.
Cisco is also investing in edge computing by integrating networking with real-time processing power at the edge, a market where NVIDIA’s Jetson platform is also dominant. Both companies address AI processing at the edge for applications like IoT and autonomous systems.
In fiscal year 2024, Cisco spent over $7.9 billion on advancing networking technologies, AI-driven network automation, edge computing, and cybersecurity solutions. [18]
5. Apple
Annual Revenue: $29.36 billion (from Mac)
Competition: A-series and M-series chips
Apple’s custom silicon, including the M1, M2, and M3 chips for its Mac lineup, directly competes with Nvidia in high-performance computing and AI workloads. In 2024, Apple introduced the M4 chip, featuring the fastest Neural Engine to date, capable of executing 38 trillion operations per second. [19]
Apple also develops A-series Bionic chips, which integrate machine learning accelerators and neural engines into iPhones, enabling powerful on-device AI processing. Their most advanced chip, the A18 Pro, boasts a 16-core neural engine that can handle 35 trillion operations per second.
Apple Bionic A18 Pro 3nm chip for iPhone 16 Pro models.
6 core CPU
6 core GPU#AppleEvent #AppleEvent24 #BionicA18Pro pic.twitter.com/1uKkQseltL— Abhishek Yadav (@yabhishekhd) September 9, 2024
In 2023, Apple sold approximately 231 million iPhones, representing 52% of its total revenue. That same year, Apple invested $29.9 billion in R&D, an increase of $3.5 billion from 2022, with a strong focus on AI, machine learning, custom silicon, and augmented reality technologies.
4. AMD (Advanced Micro Devices)
Annual Revenue: $23.7 billion
Competition: Radeon GPUs and EPYC processors in data centers.
AMD (short for Advanced Micro Devices) is Nvidia’s direct competitor in the GPU, data center, and AI hardware market. It also designs custom silicon for consoles like Xbox and PlayStation.
AMD’s Radeon series competes with NVIDIA’s GeForce series in gaming and professional GPUs. Its MI series, including MI100 and MI200 accelerators, target deep learning and AI workloads, competing with Nvidia’s Tensor Core GPUs.
Plus, its EPYC processors and Instinct GPUs offer a powerful combination for data centers, particularly in high-performance computing and cloud computing environments. The EYPC Milan series, in particular, has shown substantial performance improvements, challenging both Nvidia and Intel. [20]
AMD’s market share has increased in recent quarters. In the desktop market, AMD’s unit share is 23.9%, and revenue share is 19.2%. In the laptop market, AMD’s unit share increased from 16.2% in Q1 2023 to 19.3% in Q1 2024. However, it controls only 12% of the discrete GPU market, with NVIDIA holding the dominant share. [21]
3. Qualcomm
Annual Revenue: $37.34 billion
Competition: Mobile AI and autonomous driving systems
While Qualcomm is best known for its leadership in mobile processors and 5G technology, it has expanded into AI and automotive platforms, where it directly challenges Nvidia. Its popular Snapdragon chips power AI processing on mobile devices and IoT applications, with built-in AI engines for on-device inference
Qualcomm has also developed the Snapdragon Ride platform that delivers AI-powered solutions for driver assistance and autonomous driving. It has partnerships with automakers like General Motors, directly competing with Nvidia’s Drive ecosystem. [22]
Currently, Qualcomm is the third-largest semiconductor company, with a global semiconductor market share of 5.4%. Plus, it controls nearly 30% of the smartphone chip market. [23]
Besides chips and wireless technologies like 5G, Qualcomm generates revenue by licensing portions of its extensive intellectual property portfolio. This includes certain patent rights that are crucial for manufacturing specific wireless products.
2. Intel
Annual Revenue: $55.12 billion
Competition: CPUs, GPUs, and autonomous driving platform
Intel both designs and manufactures computer chips, whereas most of its rivals (including Nvidia) only do one or the other. It develops CPUs, GPUs, AI accelerators for data centers, and autonomous vehicle technology.
More specifically, Intel competes through its Xeon processors and AI accelerators like the Habana Gaudi2 and Nervana Neural Network processors. Its oneAPI platform provides a unified programming model across CPUs and GPUs, competing with NVIDIA’s CUDA ecosystem.
Intel holds a significant market share in the global GPU market. While Nvidia controls over 90% of the market for GPUs used in data centers, Intel dominates the integrated graphics market with a 68% market share. [24]
Intel also competes with Nvidia in autonomous driving through its subsidiary Mobileye. Mobileye’s EyeQ chips are used in advanced driver assistance systems (ADAS). These chips utilize a single camera sensor to provide ADAS features, such as adaptive cruise control, traffic jam assist, lane keeping assist, forward collision warning, and automatic emergency braking. Over 50 automakers incorporate EyeQ chips into their assisted-driving technologies. [25]
According to test benchmark results, Intel processors power nearly 71% of laptop CPUs, whereas AMD processors account for 21% of laptop CPUs detected via the tests.
However, Intel has faced several financial challenges recently, particularly due to increased competition, market shifts, and internal operational issues. In 2022, its annual revenue declined by 20%, followed by another 14% drop in 2023. To recover, the company is focusing on long-term strategies, including increased R&D investment and boosting domestic chip production.
1. TSMC (Taiwan Semiconductor Manufacturing Company)
Annual Revenue: $73.86 billion
Competition: Manufacturer of AI-enabling chips
TSMC operates in different parts of the semiconductor supply chain. Unlike Nvidia, which designs chips, TSMC manufactures them. It competes indirectly with NVIDIA in influencing the development and adoption of cutting-edge semiconductor technology.
TSMC is the world’s largest pure-play semiconductor foundry, manufacturing chips for various tech giants, including Apple, Qualcomm, AMD, and Nvidia itself. It specializes in advanced nodes like 5nm and 4nm, and is now moving into 3nm and 2nm production. TSMC’s clients use its advanced nodes to compete directly with Nvidia’s products.
The company holds nearly 53% share of the global Semiconductor manufacturing market. It is the go-to manufacturer for advanced chips, especially at 7nm and below. In 2023, TSMC shipped 12 million 12-inch equivalent wafers, with 7nm and smaller chips accounting for 58% of the company’s total wafer revenue. [26]
Read More
Sources Cited and Additional References- Agam Shah, Nvidia shipped 3.76 million data-center GPUs in 2023, HPCWire
- Press Release, Nvidia to acquire Mellanox for $6.9 billion, Nvidia
- Report, IBM Watson services market analysis, Facts.mr
- Quantum Research, The hardware and software for the era of quantum utility, IBM
- Key Insights, Marvell Technology has a total of 16347 patents globally, GreyB
- Q3 2024 Report, Synopsys posts financial results for third quarter fiscal year 2024, Synopsys
- Products, AWS Graviton processors, Amazon
- Products, AWS Inferentia accelerators, Amazon
- Products, AWS Trainium for deep learning and generative AI training, Amazon
- Hanguang 800 NPU, AI inference solution for data centers, Alibaba Group
- Blogs, Alibaba Cloud unveils chip development platform, Alibaba Cloud
- Compute, Announcing Trillium, the sixth generation of Google Cloud TPU, Google Cloud
- Company Overview, Alphabet R&D expenses, Macrotrends
- Jacob Feldgoise, Huawei’s AI chip tests US export controls, CSET
- Barry Elad, Huawei statistics by revenue and business segment, Coolest-Gadgets
- Technology & Telecommunications, DRAM manufacturers revenue share worldwide, Statista
- Technology & Telecommunications, Enterprise network infrastructure vendor market share worldwide, Statista
- Company Overview, Cisco R&D expenses, Macrotrends
- Press Release, Apple introduces M4 chip, Apple
- AMD Milan Processors, The Milan nodes are installed on the computer floor of the main NAS building, NASA
- Anton Shilov, AMD takes CPU market share from Intel in desktops and servers, tom’sHardware
- Press Notes, General Motors and Qualcomm extend long-standing relationship, Qualcomm
- Technology & Telecommunications, Qualcomm semiconductor market revenue share worldwide, Statista
- Timothy Fries, Can Intel and AMD compete with Nvidia?, Investing
- EyeQ, The SoC for automotive applications, Mobileye
- 2023 Annual Report, Letter to shareholders and financial highlights, TSMC
