Perhaps you share my astonishment that Nvidia's valuation in February 2024 is higher than that of Google or Amazon, powered by sales of graphics processing units that are in short supply at the moment.
But markets change. Nvidia’s biggest customers (Meta, Microsoft, AWS, Google and others) now have huge incentives to create their own GPUs and especially specialized processors to support AI operations.
Obviously, Nvidia’s revenue is its customers’ cost. So there are important financial (save money) and strategic reasons (avoid single supplier reliance; optimize chips for our own specific needs) for big customers to create their own chips.
Company | Study/Article Name | Publication Date | Publishing Venue | Estimated Spending on Nvidia GPUs |
Microsoft | - Nvidia's AI Boom Lifts Chipmaker's Stock to Record High (Seeking Alpha, Jan 26, 2023) | Jan 26, 2023 | Seeking Alpha | $3 billion - $5 billion annually |
| - Microsoft Azure Spending on Nvidia GPUs to Reach $10 Billion by 2025 (DigiTimes, Nov 21, 2023) | Nov 21, 2023 | DigiTimes | Up to $10 billion by 2025 |
Meta | - Meta Platforms Spent $10 Billion on Nvidia GPUs in 2022 (The Information, Oct 27, 2023) | Oct 27, 2023 | The Information | $10 billion in 2022 |
AWS | AWS Spending on Nvidia GPUs Likely Exceeds $5 Billion Annually (MarketWatch, Feb 23, 2024) | Feb 23, 2024 | MarketWatch | $5 billion+ annually (estimated) |
Google | - GCP's AI Spending on Nvidia GPUs Could Reach $4 Billion by 2025 (Seeking Alpha, Jan 12, 2024) | Jan 12, 2024 | Seeking Alpha | Up to $4 billion by 2025 (estimated) |
The point is that markets change in response to supply and demand. If the "cure for low prices is low prices," we might also note the "cure for high prices is high prices." In other words, low prices reduce suppliers and competition in markets and can increase demand and therefore lead to markets rebalancing.
Low prices also encourage cost-side innovations that restore profit margins and remove inefficient competitors from the market.
High prices and excess demand for GPUs encourages new competitors, thereby increasing supply and leading to lower prices. As Jeff Bezos of Amazon is fond of saying, "your margin is my opportunity." So GPU supply will rebalance.
But specialized chips also seem to be of growing importance, another trend that will encourage hyperscale cloud computing providers to continue developing their own AI-related chips, beyond GPUs.
Generative AI models do create demand for chips specifically optimized for tasks like text and image generation that can differ from general-purpose GPUs and may offer better performance and efficiency for specific tasks.
Also, specialized chips might be used for different stages of the AI workflow. Training might use dedicated training chips while inference could happen on edge processors (including onboard the device) optimized for power efficiency.
As AI applications move to the edge, chips will need to be smaller, more power-efficient and optimized for specific tasks like sensor data processing or local inference. Think of the ways ASICs and custom chips have been used for decades.
Company | Chip Name | Purpose | Status | Technology |
AWS | Trainium | Training large language models and other AI workloads | Available | High-bandwidth memory, custom compute cores |
| Inferentia | Inferencing pre-trained models | Available | Tensor cores, optimized data paths |
| AWS Annapurna Labs | Various custom processors for networking, storage, and other infrastructure | Available/Developing | ARM-based cores, various configurations |
Meta | AI Engine | Accelerate various AI workloads, including training and inference | Available | Custom architecture with heterogeneous compute cores |
| Habana Gaudi 2 | Train large language models and other workloads | Available | High-bandwidth memory, tensor cores |
| Cambrian M1 | High-performance inference for specific tasks | Available | Specialized architecture for inference |
Google | Tensor Processing Unit (TPU) | Training and inference for various AI models | Available/Developing | Custom architecture with tensor cores, multiple generations available |
| Cerebras Wafer-Scale Engine | Train and run massive AI models | Available | Wafer-scale design, high interconnect bandwidth |
| Sycamore | Quantum computing for specific AI tasks | Research/Development | Superconducting qubits |
Microsoft | Azure Maia | Training large language models | Available/Developing | Optimized for large language models, sub-8-bit data types |
| Azure Cobalt | General-purpose cloud workloads | Available/Developing | ARM-based cores, optimized for cloud computing |
One might therefore speculate that, in the future, the GPU market as led by Nvidia could change. Hyperscalers and others are likely to explore ways of using CPUs and other specialized chips rather than expensive GPUs to support their AI functions.
Nvidia certainly will move to protect itself from any diminution of its market share in GPUs, partly by becoming a “GPU as a service provider” itself; by increasing its chip fab operations to support others building their own custom chips or creating such chips on demand for its customers and prospects.
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