The rise of artificial intelligence is reshaping the personal computer market, and in this new scenario Apple has encountered a Mac demand it didn't expect.In the last quarter, sales of their computers have increased by 9,1%, almost four times more than the growth of the global PC market, which stands at around 2,5%.
This AI-related boom has had an immediate effect: supply voltages across much of the Mac range aimed at developers and startupsModels like the Mac mini and Mac Studio, especially in configurations with more unified memory, are accumulating weeks of delay and are beginning to become an operational bottleneck for teams that rely on local inference.
Mac grows by 9,1%: the push of local AI
Sales figures show that the Mac It grows by 9,1% compared to 2,5% for the rest of the PC marketThis indicates that Apple is capturing a significant portion of the demand for AI hardware. The leap is remarkable considering that the Mac mini recently represented a mere 3% of total Mac sales, a virtually negligible niche within the product line.
What has changed is the use: More and more founders and technical teams are looking for machines capable of running AI models locally.without relying so heavily on cloud services and without incurring the recurring costs of GPU servers. For many startups, especially in their early stages, the Mac mini and Mac Studio have become the foundation of their testing lab.
The interest doesn't come only from the United States. In Spain and other European markets, access to capital for large cloud deployments is more limited. In Silicon Valley, a desktop computer with good inference performance is more affordable than setting up your own GPU infrastructure or committing to high spending on AWS, Azure, or GCP.
In parallel, the emergence of Apple Intelligence and more powerful chips for AI tasks, such as the M4 and M5 series, has reinforced the idea that The Mac ecosystem is an attractive option for AI product development and prototyping.even when the scaling phase is later taken to the cloud or other platforms.
Most affected models: Mac mini and Mac Studio in the spotlight
The less pleasant side of this boom is that Not all Macs are normally availableDemand pressure and a shortage of unified memory have caused the most interesting AI models to suffer particularly.
In the case of the Mac mini, the model with the M4 chip is the one that is most affected by the situation. Configurations with 16 GB or more of RAM are the most in demand.Because they allow working with models of between 7.000 and 70.000 billion parameters while maintaining good energy efficiency. For many teams, it's the balance between power and cost.
At the same time, it is observed Delivery times range from 1 to 4 weeks for the base model with 16GB of RAM and prices above $699 on secondary markets like eBay. In 32GB or 64GB configurations, some retailers list these Mac minis as unavailable or with lead times as long as 12 weeks.
Mac Studio is experiencing a similar situation. Versions with high-end chips and large amounts of unified memory Models like the M4 Max, M3 Ultra, or the 128GB and 256GB RAM variants are selling out fast. Apple has acknowledged significant delays, and in some markets, these devices have been listed as out of stock for months.
In parallel, The roadmap for the new models exacerbates the feeling of scarcity.The Mac Studio with the next-generation chip (M5 Ultra) was initially planned to be presented in the summer around WWDC, but its arrival has reportedly been moved to October 2026, in the midst of a reorganization of the professional range.
The shortage of unified memory and the delay of new Macs
Much of the problem can be explained by the way Apple designs its equipment. Macs with Apple Silicon use soldered unified memory, manufactured to very specific specifications.This means that any strain on the supply chain has a direct impact on production.
According to various industry analyses, Demand for RAM modules compatible with Apple's architecture has skyrocketed.Configurations starting at 16 GB are the most difficult to supply, and this affects both the higher-end ranges and the mid-range models that many developers choose to work with AI.
A second factor is added to this context: the generational shift towards M5 chipsApple is reportedly gradually reducing production of some current models as it prepares for the launch of the new generation, leaving a narrow gap between remaining stock and current market demand.
Thirdly, the rise of local AI tools—from Llama 3.1-based assistants to Mistral models or open-source solutions—has led to Many startups prioritize machines like the Mac Studio for intensive inference.That combination of power and energy efficiency makes them a coveted asset for training, testing, and early deployment.
Apple is reacting with moves in the value chain: additional supply agreements with chip manufacturers and the launch of new production lines, including a plant in Houston (Texas) that should be fully operational by the last quarter of 2026. However, these measures will arrive, at best, when much of the year is already well underway.
Why startups are betting on Mac for local AI
Beyond the sales figures, the founders' interest in the Mac ecosystem It has a fairly clear technical and economic basis.It's not just about design or operating system, but about the total cost of ownership and how Apple has integrated the CPU, GPU, and memory.
First, the cost. A Mac mini with 16 GB of RAM costs around $1.499 In development-oriented configurations, this is significantly less than the $3.000 to $10.000 that a server with a dedicated GPU designed for continuous local inference can cost. For a pre-seeded or seeded startup, this difference translates into the ability to extend the runway by several months.
Secondly, unified memory. The design of chips like the M4, optimized for AI tasks, offers high bandwidth with relatively low thermal consumption.This allows for better use of each gigabyte of memory when loading large models, something that doesn't always happen in traditional architectures with separate CPUs and GPUs.
And thirdly, there is the energy component. The efficiency of this equipment allows modern language and image models to be run with a controlled electricity consumption.This translates into more reasonable electricity bills, less need for cooling and, in general, less noise and complexity when setting up a small cluster in an office or even at home.
In practice, this is leading to Many projects start with a local infrastructure focused on Mac. To quickly validate the product before making the leap to more expensive cloud solutions. Once the product-market fit is found, the heavy lifting is migrated to cloud servers or workstations with NVIDIA GPUs, while Macs remain the primary development environment.
The role of Spain and the European and Spanish-speaking ecosystem
In Spanish-speaking countries, the dynamics have their own nuances. In Spain, Latin America, and much of Europe, access to financing for large-scale cloud infrastructure is not so easy. as in the major technology hubs of the United States or Asia. This gives local hardware added importance.
For small teams, A Mac mini or a Mac Studio becomes the most accessible entry point to AI developmentThey don't require bulky contracts with cloud providers, relying on promotional credits that run out, or dealing with volatile cloud GPU prices.
However, this very dependence creates its own problems. Import times to Latin America from Europe or the United States They lengthen the wait even further when there is no local stock: it is not uncommon for a team with orders of Macs with high RAM to have to deal with delays of several weeks just due to logistics.
Furthermore, in many countries of the region, Official stores and authorized distributors operate with high margins and markups.This further increases the cost of accessing AI-oriented models. As a result, some startups are turning to gray markets or parallel acquisitions, assuming risks related to warranties and technical support.
In the case of Europe, supply policies also play a role. Priority is usually given to markets with the highest sales volume.Therefore, not all countries receive the same quantities of Mac mini or Mac Studio in high-memory configurations. For Spanish teams wanting to secure key hardware, it's not uncommon to coordinate centralized purchases in other EU countries where more stock is available.
How are Apple and startups reacting to supply pressure?
Faced with this clash between demand and production capacity, Apple has made a move. The company has closed deals to secure tens of millions of additional chips And it has announced an industrial expansion that includes new capacity in the United States. The goal is to stabilize the supply of Macs with Apple Silicon by the end of 2026.
However, in the short term The reality for teams that need high-performance machines is less optimistic.Waiting lists for Mac Studio with lots of RAM or Mac mini with configurations above 16GB are still there, with availability windows that can extend from 4 to 12 weeks depending on the model and market.
Given this scenario, many startups are opting to rethink your hardware roadmapInstead of waiting for the "perfect" configuration, the purchase of available base models and the use of mixed solutions are becoming more widespread, combining several mid-range Macs with cloud resources for heavier workloads.
Another common reaction is the turn towards a hybrid architecture that combines local inference and remote trainingMacs are reserved for day-to-day development, quick testing, and lightweight deployments, while training larger models or batch processing is outsourced to on-demand GPU services.
A more prudent planning mindset is also taking hold: Teams that previously bought hardware at the last minute are now reserving a margin of 2 to 3 months. to secure the machines they will need for the next product milestone. The current shortage, in that sense, serves as a wake-up call.
Mac vs. PC with Windows and dedicated GPUs
The Mac's differential growth may give the impression that Apple completely dominates the AI ​​hardware field, but The real picture is more nuancedWhile Macs are gaining ground in development and local inference, Windows PCs with NVIDIA GPUs remain the benchmark for many intensive workloads.
In large model training tasks or projects that require a intensive and continuous use of dedicated GPUsWorkstations with graphics cards like the RTX 4090 maintain a very competitive price-performance ratio. For some startups with tight budgets, building a tower with a powerful GPU can be more cost-effective than investing in a high-end Mac Studio.
However, the decision is rarely binary. Many teams combine Mac and PC in their infrastructureThey use Apple laptops and desktops for development, code management, and initial inference testing, and reserve PCs with dedicated GPUs or cloud services for specific phases of the model's lifecycle.
In Spanish-speaking environments, this Platform mixing is becoming the most flexible optionA small studio in Madrid or Mexico City can work daily with Mac mini, Mac Studio or MacBook Pro, and temporarily set up GPU instances in the cloud when it needs to train a model or perform a specific high-consumption task.
The result is an architecture where The Mac positions itself as the stable base of operationsWhile the most expensive resources are activated only when needed. This makes it easier to balance budgets and reduces the risk of over-provisioning the infrastructure before the product is truly validated.
With all this context, the picture that emerges is that of a market in transition: Macs are growing thanks to the pull of AI, but that same success has exposed the seams in the supply chain.For startups and developers in Spain, Europe, and Latin America, the challenge lies in finding a balance between relying on the Apple ecosystem for local inference and not being trapped by a lack of stock, by planning further in advance, combining platforms, and leaving the door open to the cloud and alternative hardware when the situation requires it.