A Computer for AI: What You Need to Run AI Models Locally — VRAM, RAM, NPUs and Realistic Expectations

AI has become part of work: language models help write text and code, image generators create illustrations, and speech recognition turns meeting recordings into text. Most people use cloud services, but more and more want to run models locally — no monthly subscriptions, no limits and no passing confidential data to third parties. That requires a suitable computer. Let's look at which hardware really matters for AI and what to expect from a home AI computer.

Video memory is what matters most

AI runs mainly on the graphics card, and the key spec is the size of its memory (VRAM). The model has to fit entirely in video memory, or it slows down many times over or won't run at all. Rough guidelines:

• 8 GB — image generation in basic modes, small compressed language models, speech recognition.
• 12–16 GB — comfortable image generation, mid-sized language models, simple video generation.
• 24 GB and more — large language models, training and fine-tuning small models, video generation.

So for AI, a graphics card with more memory is often more useful than a faster one with less. NVIDIA cards are the best supported by AI software; AMD and Intel cards also work, but with more setup and more limitations.

RAM and storage

You need at least 32 GB of RAM, and 64 GB is better: models are loaded through it, and some programs move work into system memory when video memory runs short. Models take up a lot of space — from a few to dozens of gigabytes each — so you need a fast 1–2 TB NVMe SSD. A slow drive noticeably increases model loading times.

Processor and NPU

For most AI tasks the processor is secondary: a modern 6–8-core chip is enough. Recent years have brought "AI laptops" and processors with a built-in neural processing unit (NPU). An NPU runs small tasks efficiently — background blur on video calls, noise suppression, assistant features in Windows — but it's no substitute for a powerful graphics card for image generation and large language models. A separate case is computers with unified memory, where the processor and graphics share one large pool: they can run large language models, though more slowly than a powerful graphics card.

Cooling and power

AI loads the graphics card to 100% for hours — when generating a series of images or processing long recordings. That's harder than gaming. You need a well-ventilated case, good cooling and a power supply with headroom. An overheated graphics card throttles and runs slower, and a weak power supply causes shutdowns under load.

Cloud or your own computer

Cloud services give access to the most powerful models with no hardware investment, and for occasional tasks they're better value. Your own computer makes sense if you use AI every day, work with confidential data (client documents, medical or legal material), want full control over models and settings, or generate a lot of images. Be realistic about capabilities: local models that fit in a home graphics card fall noticeably short of the largest cloud ones.

Software

Running models needs up-to-date graphics drivers, compute libraries and front-end programs for language models and image generation. Many install in a few clicks, but driver and library versions must be compatible with each other, or models won't start or will run on the processor dozens of times slower. Only install models and programs from trusted sources: malware is also spread disguised as popular AI tools.

Common mistakes

• Buying a fast graphics card with 8 GB of memory for large models — not enough VRAM.
• 16 GB of RAM — the system constantly uses the page file.
• Models on a slow hard drive — long loading times.
• A weak power supply and a cramped case — shutdowns and overheating during long runs.
• Expecting an "AI laptop" with an NPU to generate images like a powerful graphics card.

What our technician does

1. Finds out which models and tasks you plan to run and chooses a configuration to suit them.
2. Builds a computer or upgrades your existing one: graphics card, memory, SSD, cooling and power supply.
3. Installs drivers, compute libraries and programs for running models locally.
4. Checks that models run on the graphics card rather than the processor, and tests stability under sustained load.
5. Sets up model storage, backups and security.

A real example

A law firm in Vake wanted to use a language model to analyse contracts but couldn't send client documents to cloud services. Our technician built a workstation with a 24 GB graphics card, 64 GB of RAM and a 2 TB NVMe SSD, installed a program for running models locally and set up access to it from the lawyers' computers over the local network. Documents are now processed inside the office, and a first review of a standard contract takes minutes instead of an hour.

It also pays to think about electricity and noise. A graphics card running at full load for hours draws several hundred watts and its fans are clearly audible, so a computer for AI is best placed where its noise won't bother anyone, and protected by a UPS so a power cut doesn't interrupt a long job halfway through.

Call a technician

If you want to build or upgrade a computer for AI, we'll come to any district of Tbilisi around the clock. Learn more on our custom PC build, component upgrades and software installation pages.