Nvidia Dominance Under Threat: AI Inference and the Rise of Alternative Chips

Nvidia Corporation (NASDAQ: NVDA), once the undisputed leader in AI computing and the third-ever American company to surpass a $3 trillion valuation, is now facing significant challenges. With the rapid evolution of artificial intelligence, industry experts are questioning the sustainability of Nvidia’s dominance as AI workloads shift towards inference-based applications. The emergence of cost-effective, energy-efficient alternatives has put Nvidia’s high-margin GPU business under scrutiny.

The AI Evolution: From Training to Inference

AI development consists of two critical phases: training and inference. While training large-scale models requires immense computational power—where Nvidia’s H100 and A100 GPUs shine—AI inference, or the process of making real-time predictions, often demands lower-cost, efficient solutions. According to Frank Palermo, former Principal Engineer at IBM, the future of AI will be heavily weighted towards inference, making Nvidia’s power-hungry GPUs less necessary.

As hyperscalers and startups race to build AI solutions optimized for inference, Nvidia faces increasing pressure to adapt. Companies are searching for cost-effective alternatives that offer competitive performance without the exorbitant costs associated with Nvidia’s hardware.

Investment Downsides: The High Cost of Nvidia’s GPUs

Nvidia’s dominance in AI computing comes at a significant price. The company boasts an impressive trailing twelve-month (TTM) EBIT margin of nearly 62%, reflecting its high profitability. However, this profitability is driven by the steep costs imposed on enterprises and startups relying on Nvidia hardware.

• The Cost of Nvidia’s H100 GPUs: A single H100 GPU costs approximately $30,000 upfront.
• Leasing Costs: Companies renting H100s for continuous AI workloads face an annual cost of nearly $48,000 per GPU.

For cash-rich startups and tech giants, these costs may be manageable, but for smaller enterprises and research institutions, they present a major barrier. As AI adoption accelerates, organizations are actively seeking cost-efficient alternatives to Nvidia’s offerings.

The Rise of AMD: A Credible Challenger to Nvidia

One of Nvidia’s strongest competitors, Advanced Micro Devices (AMD), has strategically positioned itself as a viable alternative. While AMD’s AI chips may not yet surpass Nvidia’s in absolute performance, they offer a compelling price-to-performance ratio.

• AMD’s MI325X AI GPU was launched in October 2024 and has already started shipping to customers, beating Nvidia’s delayed B100 and B200 GPUs.
• Flexible Infrastructure: Unlike Nvidia, AMD does not enforce strict server and rack design requirements, offering enterprises greater flexibility in deployment.
• Major Customers: AMD has secured partnerships with OpenAI, Meta, Microsoft, and Google, signaling confidence in its AI hardware solutions.

AMD’s next-generation Instinct MI350 is expected to launch in the second half of 2025, promising further advancements in AI computing. If AMD continues to refine its AI hardware while maintaining competitive pricing, it could gain significant market share from Nvidia.

The Hyperscaler Shift: Custom AI Chips as GPU Alternatives

Beyond AMD, the largest cloud providers—AWS, Google Cloud, and Microsoft Azure—have developed proprietary AI chips to reduce reliance on Nvidia. These custom chips offer a compelling alternative for businesses looking to cut AI infrastructure costs.

• Amazon Web Services (AWS): Launched Trainium2 in 2024, an AI accelerator tailored for training and inference tasks.
• Google Cloud: Introduced Trillium, designed to optimize performance for AI workloads while reducing power consumption.
• Microsoft Azure: Developed Maia 100, an in-house AI chip aiming to streamline generative AI processes.

These companies have the financial resources and expertise to develop AI chips that meet their specific needs, reducing the necessity for Nvidia’s hardware.

The Startup Disruption: Energy-Efficient AI Processors

In addition to hyperscalers, several startups are creating custom AI chips that challenge Nvidia’s dominance. These emerging players focus on designing hardware optimized for AI inference, offering significant advantages in power efficiency and cost-effectiveness.

• Cerebras Systems: The US-based startup has developed the Wafer-Scale Engine (WSE), a non-GPU architecture integrating compute, memory, and interconnect fabric on a single piece of silicon. Its latest third-generation WSE set a world record for AI inference performance in November 2024 while consuming just 15 kW per system. Customers include U.S. national laboratories and pharmaceutical firms.
• Groq, Mythic, and Graphcore: These companies are advancing AI accelerators focused on ultra-low latency inference and energy-efficient processing.
• Horizon Robotics and Cambricon: China-based chip startups specializing in AI hardware optimized for real-world applications, from autonomous driving to edge computing.

With an increasing number of companies looking for alternatives to Nvidia’s expensive GPUs, these startups are well-positioned to capture market share in AI inference workloads.

Supply Chain Issues: A Vulnerability for Nvidia

Beyond pricing and competition, Nvidia has faced major supply chain setbacks. A design flaw in its upcoming B200 GPU led to disruptions, delaying shipments and forcing enterprises to look elsewhere. Diversifying chip suppliers has become a critical strategy for companies to mitigate supply chain risks while optimizing AI infrastructure costs.

Nvidia’s Response: Can It Adapt?

Despite these challenges, Nvidia remains a leader in AI computing. The company is actively investing in next-generation GPUs and software optimizations to maintain its competitive edge. However, with increasing competition from AMD, hyperscalers, and AI-focused startups, Nvidia must navigate a rapidly shifting market landscape.

As AI inference gains prominence and cost-sensitive enterprises seek alternatives, Nvidia’s ability to adapt will determine whether it retains its AI dominance or cedes ground to emerging players.