Blog · 17 September 2026 · 7 min read
Local AI: Mac or GPU server?
Choosing the hardware is the first decision in a local AI project. There's no single right answer for everyone: it depends on how many people will use the AI, which models you need and where it will be installed.
In short: a Mac with Apple Silicon suits offices and small to medium teams, because unified memory lets you load large models on a quiet, low-power machine. A GPU server makes sense when many users use the AI at the same time, when you need maximum performance, or when the hardware has to go in a rack or a data centre. Either way, the deciding factor is the memory available to the model.
Memory decides which models you can use
An artificial intelligence model must fit entirely in memory to respond quickly. As a reference, a model with around 30 billion parameters compressed to 4 bits takes up about 20 GB, and one with 70 billion over 40 GB.
- On Macs with Apple Silicon memory is unified: the same RAM is used by the processor and the GPU. A 2026 Mac Studio starts at 36 GB (M5 Max) and at 96 GB with the M5 Ultra, with larger memory configurations available on request.
- On servers what counts is the graphics card memory (VRAM). To get a lot of memory you fit several cards, with costs, power consumption and heat rising accordingly.
Quick comparison
| Mac with Apple Silicon | GPU server | |
|---|---|---|
| Large models | Yes, thanks to unified memory | Yes, with multiple cards or professional cards |
| Simultaneous users | Few to moderate | Many |
| Response speed | Good for office use | Maximum |
| Power consumption and noise | Low, suitable for an office | High, best in a server room |
| Installation | On a desk or in a cupboard | Dedicated rack, power and cooling |
| Entry cost | From €3,049 (Mac Studio M5 Max) | On quotation, highly variable in 2026 |
A real example
At Dabryx we use a Mac Studio with an M1 Ultra chip and 128 GB of unified memory. It runs open models with 35 and 122 billion parameters, a chat with personal accounts, document reading, image generation and automations, securely accessible from the team's smartphones too over a private network.
It's a configuration from a few generations ago: 2026 Mac Studios offer newer chips and even larger memory configurations.
How much memory is really needed?
You need memory for all the models you want to keep ready at the same time, plus a margin for the system and for long documents. The sum is done like this:
- A single text model, used by one person or a small group: memory that holds that model with a good margin is enough.
- Several models ready at the same time (one for reasoning, a fast one for simple requests, one for images): their sizes add up. With little memory the system unloads one to load the other, and each switch costs a wait of a few tens of seconds.
- Long documents and extended conversations: the text the AI keeps in mind takes up extra memory, and it grows with the number of people connected.
Extra memory does not make answers faster: it avoids loading waits and leaves room for the models that will come out over the next few years. Since it cannot be added later on a Mac, it is the item most worth thinking through before buying.
One user or several users?
You can start with just one person and add the others later, without reinstalling. Each user has their own login, their own conversations and the documents they are authorised to see.
What changes is the load: when two people make a heavy request at the same moment, the answers slow down, because there is one model and it is working for both. With a single user the problem does not exist. With a group it is worth measuring actual use in the first few weeks and deciding afterwards whether a larger machine or a GPU server is needed.
How to choose
- Up to a few dozen people, office use (questions about documents, writing, summaries): a Mac with plenty of memory is usually the simplest and most cost-effective choice.
- Many simultaneous users, integration into applications with constant traffic or a need for maximum performance: GPU server.
- Targeted tasks (sorting emails, extracting data from documents): a small model on the hardware you already have is often enough.
Before buying anything, it's worth testing the models on your own documents: it's the most reliable way to understand how much memory you really need.
About the author: Dabryx
Dabryx is an Italian software house that installs local AI in companies, on site across Italy, on servers or Macs. The basic installation starts at €2,900, hardware not included, and consulting at €100 per hour. The office is in Rocca Priora, near Rome.
We also install local AI in Rome, Milan, Parma and Reggio Emilia, and we develop custom management systems and custom software.
Frequently asked questions
Is a Mac mini enough for a local AI?
For small models and targeted tasks it may be enough. For larger models and more users you need more memory, and the Mac Studio offers much larger configurations.
Can you start with a Mac and move to a server later?
Yes. Tools such as Ollama and Open WebUI run on both: models, configurations and automations can be moved as usage grows.
Is an internet connection required?
Not to use the AI: processing happens on site. The internet is only needed to download model and software updates.
Want an estimate for your company?
Tell us how many people will use the AI and what for: we'll tell you which configuration you need.
Response times
Within 2 hours on business days (9:00–18:00) for clients with a support contract.
AI consulting
Basic installation from €2,900, consultancy from €100 per hour, on your company's server or Mac.
Where we work
Throughout Italy: we come on site to the client's premises and also support you remotely.