Apple and Nvidia: A Complex Tale of Estrangement and Strategy
In the era of AI explosion, Nvidia has almost monopolized the AI chip market with its powerful GPU chips and become a sought-after partner for many tech giants. However, Apple has always maintained a delicate distance from Nvidia and even deliberately avoided it. What lies behind this “love-hate relationship” and strategic considerations?
**I. Historical Grudges: From “Honeymoon Period” to “Ice Age”**
1. Early Collaboration
As early as 2001, Apple used Nvidia chips in its Mac computers to enhance graphic processing capabilities. The relationship between the two parties was good and they were in a “honeymoon period.”
2. Relationship Rift
– In the mid-2000s, Steve Jobs publicly accused Nvidia of stealing technology from Pixar Animation Studios (of which Jobs was a major shareholder at the time), casting a shadow over the relationship between the two parties.
– In 2008, a batch of defective GPU chips produced by Nvidia were used in several notebook computers including Apple’s MacBook Pro, triggering a large-scale quality problem known as the “bumpgate” incident. Nvidia initially refused to take full responsibility and compensate, angering Apple and leading to the breakdown of their cooperation. Apple had to extend the warranty period of affected MacBooks, suffering huge economic and reputational losses.
– It is revealed that Nvidia executives have long regarded Apple as a “demanding” and “low-profit” customer and are reluctant to invest too many resources in it. After the success of the iPod, Apple also became stronger and believed that Nvidia was difficult to cooperate with. In addition, Nvidia’s attempt to charge licensing fees for the graphic chips used in Apple’s mobile devices further intensified the conflict.
**II. The Game of Business and Technical Strategies**
1. Controlling the Ecosystem
Apple has always emphasized comprehensive control over the software and hardware of its products and strived to create a complete ecosystem. To achieve this goal, it continuously strengthens its independent research and development capabilities and reduces its dependence on external suppliers. In the chip field, from the iPhone’s A-series chips to the Mac’s M-series chips, Apple has continuously launched high-performance self-developed chips, gradually getting rid of its dependence on traditional chip giants such as Intel. In the field of AI chips, naturally, it is unwilling to be controlled by Nvidia.
2. Ensuring Dominance
Apple hopes to have complete control over key technologies to ensure optimized product performance and differentiated competitive advantages. Purchasing a large number of Nvidia’s GPUs would weaken Apple’s dominance in the AI field and limit its product innovation and technical routes.
3. Power Consumption and Heat Dissipation Issues
Nvidia’s GPUs are powerful but have problems of high power consumption and large heat generation. This is a challenge for Apple products that pursue thinness and portability. Apple has always been committed to making its products lighter, thinner, and more efficient, while Nvidia’s GPUs are somewhat contrary to its design concept. Apple has repeatedly asked Nvidia to customize low-power, low-heat GPU chips for its MacBook but has not succeeded, prompting Apple to turn to AMD and cooperate with it to develop custom graphic chips. Although AMD’s chips are slightly inferior in performance to Nvidia’s, they are more in line with Apple’s needs in terms of power consumption and heat dissipation.
**III. New Challenges in the AI Wave**
1. Training Needs
In recent years, the explosive development of artificial intelligence technology has brought new challenges to Apple. To maintain competitiveness in the AI field, Apple needs to train larger-scale and more complex AI models, which undoubtedly requires more powerful computing power and more GPU resources.
2. Strategies to Get Rid of Dependence
– Apple mainly leases Nvidia’s GPUs through cloud service providers such as Amazon and Microsoft instead of purchasing them in large quantities to avoid large capital investment and long-term dependence.
– Apple has used AMD’s graphic chips and cooperated with Google to use its TPU (Tensor Processing Unit) for AI model training.
– Apple is collaborating with Broadcom to develop its own AI server chip, codenamed “Baltra”, which is expected to be put into mass production by 2026. This chip is not only used for inference but also possibly for training AI models.
Although Apple has been trying hard to get rid of its dependence on Nvidia, in the short term, the competitive and cooperative relationship between the two parties may still exist for a long time. Mastering core technologies is the key to remaining invincible in the fierce market competition.
