CIOInsider India Magazine

The battle for chip market supremacy in the global semiconductor industry is becoming fierce while each displays their chip innovation leadership. Depending on how the innovation is directed, the rivalry may either encourage or barricade advancements in the field. In case it encourages, fields like quantum computing, 5G communications, artificial intelligence, and other cutting-edge technology are bound to prosper. On the other hand, chances are high for duplication, inefficiency, and eventually slowing down innovation when many players pursue the same objectives and technology all at once.

For today’s competitors, the round has industry titans, namely NVIDIA, Intel, AMD, and Qualcomm, fighting for supremacy across a wide range of applications. Not to forget the ongoing geopolitical tensions surrounding the US and China's struggle for technological domination, while the industry runs the risk of further shattering global supply chains and upsetting cross-border trade.

Even if NVIDIA's sales are rising significantly, Qualcomm, AMD, and Intel are still tough opponents in the ring. Where are these companies faring and failing? Let’s find out!

Nvidia
Stronger than ever, Nvidia is preparing new AI chip architectures yearly instead of every other year, and to release new software that might further integrate its chips into AI applications. It is leading in AI acceleration with the H100 CPU, powering activities like building massive language models. Its position in data center networking is even more stronger after taking Mellanox under its wing. But the challenges lie in limited CPU presence and reliance on particular workloads.

For edge computing it is focusing Jetson modules on edge and AI applications. AI workloads on-premise take care of its DGX A100 platforms. One of the challenges is the competition from ARM-based low-power alternatives.

To upgrade its cloud with AI services it has joined hands with prominent companies such as Amazon and Microsoft. Internally its AI development are powered by its DGX systems. The possibility of AI acceleration becoming commoditized and hyperscalers creating their own chips are challenges.

With its Core CPUs, Intel maintains a commanding lead by meeting a variety of performance requirements. The most current processors at Raptor Lake and Alder Lake have excellent efficiency and performance. But there are obstacles, like AMD's rivalry and possible production constraints.

It provides a wide range of products (Stratix FPGAs, Ponte Vecchio GPUs, and Xeon CPUs) to meet the needs of various workloads. The landscape might be disrupted by its foundry aspirations. But there are worries about 7nm technology delays and intense rivalry.

Intel
In addition to Atom and Xeon CPUs, Intel is providing Movidius visual processing units (VPUs) for edge applications. For Project Athena, the company is focusing on Edge AI. But power efficiency and competition from other players are challenges.

It provides FPGAs and CPUs to large hyperscalers, and its GPU, the Ponte Vecchio, is aimed at AI and HPC tasks. However, the competition and the possibility for hyperscalers to design custom chips, especially for AI applications, are its hurdles.

It offers a range of solutions including Agilex FPGAs for network infrastructure and Xeon CPUs, Atom processors for network edge, and Pentium processors for CPEs. The adoption of open-source technologies by Telcos, especially for edge computing workloads, presents challenges due to competition.

AMD Takes on Nvidia
Advanced Micro Devices unveiled new AI processors and a roadmap for creating AI chips over the following two years in an effort to challenge industry leader Nvidia. At the Computex technology trade show in Taipei, AMD CEO Lisa Su introduced the MI325X accelerator. The release is slated for the fourth quarter of 2024. The demand for advanced CPUs in AI data centers to manage these complex applications has increased dramatically as a result of the push to develop generative AI applications.

AMD also revealed the MI350 chip series, which is anticipated to hit stores in 2025 and is based on a cutting-edge semiconductor architecture. AMD said that compared to the MI300 series of AI chips now available on the market, it expects the MI350 to perform 35 times better in inference—the process of creating generative AI responses. Additionally, AMD introduced the MI400 series, which is based on the "Next" architecture and will launch in 2026.

Nvidia Overtook Intel in Sales, But Intel Proved in Training Models
Nvidia overtook Intel last year in sales, but the latter’s name still holds strong in AI. Take the Gaudi 3, for instance; it’s the company's third iteration of its AI accelerator. Intel also claimed that it was faster at training models and that it was a more affordable option that outperformed Nvidia's H100 in terms of performing inference.

TSMC’s Calculated Risks Sets it Apart from Samsung
Taiwan Semiconductor Manufacturing Co. differs from Samsung with its knack for taking calculated risks. It's also building its factories first while it lies in wait for demand from customers to follow pace. This has proven crucial to TSMC's ladder of success, along with its long-standing supremacy.

Samsung Electronics’ Power Lies in Chip Foundry Division
The goal of Samsung Electronics Co.'s chip foundry division is to challenge Taiwan Semiconductor Manufacturing Co. (TSMC), the market leader, and grow its production capacity through innovative manufacturing techniques. By 2025, Samsung aims to add more applicability across many domains by introducing 2-nm production for mobile phone components. Additionally, it is growing output in Taylor, Texas, and Pyeongtaek, South Korea, to bolster its foundry segment, which provides contract-based services to clients. Within the next five years, Samsung hopes to exceed TSMC in terms of state-of-the-art semiconductor fabrication technologies.

Qualcomm Has Cemented its Position in Android Smartphones
With its Snapdragon chips, found in several Android smartphones, Qualcomm offers cutting-edge performance and AI capabilities. However, Qualcomm’s obstacles consist of rivalry with Apple's own CPUs and expansion beyond iPhones.