Local AI Hardware Requirements: CPU, GPU & RAM Guide

Local AI hardware requirements come down to one spec more than any other: memory. RAM decides whether a model loads at all, VRAM does the same job on a PC with a dedicated graphics card, and CPU and storage matter, but neither one determines whether you can run a model in the first place. If you remember one thing from this guide, remember that.

Let's go through CPU, GPU, RAM, and storage one at a time, so you know exactly what you need before you download your first model.

This version drops straight into the keyword and the direct answer in sentence one, which is what you want for AI Overview eligibility and to match how "local AI hardware requirements" gets searched. It also keeps the H1 and the opening line tightly connected instead of easing in.

RAM vs VRAM: Why This Distinction Trips People Up?

VRAM is the memory built directly onto a graphics card, and it's a separate, smaller pool from your computer's regular system RAM. On a Windows or Linux PC, an AI model has to fit inside VRAM to run at full speed. If it doesn't fit, the extra layers spill over into system RAM, and inference slows down dramatically, sometimes to a crawl.

This is where Apple Silicon works differently, and it's worth understanding even if you're not on a Mac, because it explains why RAM numbers you see quoted for Macs and PCs aren't directly comparable. On an M-series Mac, there's no separate VRAM pool. CPU and GPU share one block of unified memory, so a 32GB Mac effectively has 32GB of "VRAM" available for a model, something you'd otherwise need a dedicated graphics card to match on a PC.

So when you're comparing hardware, always ask which number you're looking at. "16GB RAM" on a Windows spec sheet usually just means system memory, and the real ceiling for AI is whatever VRAM sits on the GPU. On a Mac, the RAM figure is the whole story.

RAM (or Unified Memory): The Spec That Makes or Breaks Local AI

A model has to fit entirely in memory to run. That's the whole rule. An AI model is really just a giant file of numbers, and your computer needs enough memory to hold that entire file while it's generating answers for you.

Here's roughly what different memory amounts unlock:

• 8GB: Small models in the 1B to 3B parameter range. Fine for quick tasks, but you'll hit walls fast.

• 16GB: The realistic starting point. Handles 7B to 8B models comfortably, which covers most everyday chat and writing tasks.

• 32GB: Opens up 14B to 32B models. Noticeably sharper reasoning and better code generation.

• 64GB or more: Runs 70B-class models, the closest thing to frontier-level quality you can get without a cloud connection.

On a Mac, this gets simpler because of that unified memory architecture. There's no separate pool for the CPU and another for the GPU, and the AI model pulls from the same memory your apps and browser tabs are using. That's part of why a 64GB Mac can load a 70B model that would otherwise need a multi-GPU setup on a Windows machine.

One catch worth knowing: you don't get to use all of your RAM for a model. Some of it is always reserved for the operating system, background processes, and the KV cache, which stores context as a conversation gets longer. A good rule of thumb is to leave 20 to 30 percent of your total memory unused when picking a model size, and budget more headroom if you plan to use long conversations or large documents.

How Quantization Changes the Numbers?

Quantization is what lets a 70B model fit into memory that would otherwise need to be twice as large. It works by storing each parameter with fewer bits, which shrinks the file size and memory footprint in exchange for a small, usually negligible, drop in output quality.

A rough rule of thumb: a model needs about 2GB of memory per billion parameters at full 16-bit precision. Standard 8-bit quantization cuts it roughly in half, and 4-bit quantization, the most common format for everyday use, cuts it to around a quarter. That's why a 7B model, which would need close to 14GB at full precision, comfortably runs in 4 to 5GB once quantized to 4-bit. Most local AI models built for Apple Silicon default to 4-bit quantization for exactly this reason, since it's the sweet spot between quality and what actually fits on consumer hardware.

CPU: What It's Actually Doing During Inference

The CPU handles the setup work, not the heavy lifting. It loads the model into memory, manages the request you type in, and coordinates everything happening behind the scenes. The actual number-crunching that generates each word of a response is mostly handled by the GPU.

That said, a slow CPU still creates a bottleneck. If you're on an older chip, you'll notice longer pauses before a model starts responding, even if your memory is plenty. Any Apple Silicon chip from the M1 onward handles this part well, since Apple built these chips with AI workloads in mind from day one. You don't need to chase the newest chip generation just for CPU performance. Memory capacity matters far more for what you're able to run at all.

GPU: Why Macs Don't Need a Separate Graphics Card?

The GPU does the actual math behind every word a model generates. On a traditional Windows PC, this means you need a discrete graphics card with its own VRAM, separate from your system RAM. If a model is too big for that card's memory, it simply won't run at full speed, no matter how much system RAM you have sitting unused.

Apple Silicon skips this problem entirely. The GPU built into every M-series chip taps into the same unified memory as the rest of the system, using Apple's Metal framework for acceleration. This is why a Mac with 32GB of unified memory can comfortably run models that would need an expensive dedicated graphics card on a PC.

For local AI specifically, this is one of the biggest advantages of Apple Silicon. You're not choosing between a good CPU and a good GPU and hoping they have compatible memory pools; it's already sorted out for you. This is also why running models through MLX, Apple's framework built specifically for Apple Silicon, tends to deliver noticeably faster performance than older, more generic formats.

Storage: How Much Space Local AI Needs?

Model files range from around 1GB for tiny models up to 40GB or more for the largest ones you'd realistically run at home. A 7B model at standard quantization typically lands around 4 to 5GB, while a 70B model can take up 40GB or more, depending on compression.

If you plan to keep more than one or two models around, which most people end up doing once they start experimenting, budget at least 100GB of free space. SSD speed also matters here more than people expect. A model loading off a fast internal SSD will start responding noticeably quicker than one loading from a slower external drive, since the entire file has to be read into memory before your first response appears.

Hardware Requirements by Model Size

 

That last row is worth a note. Mixture-of-experts models look intimidating on paper because of their total parameter count, but they only activate a fraction of those parameters for any single response. The catch is that every parameter still has to be loaded into memory even though only a portion gets used, so the memory requirement is based on the full size, not the active portion.

For a closer look at which specific models fit which memory tier, this breakdown of the best local AI models for Apple Silicon, ranked by RAM tier with real benchmarks, is worth a read.

Is Local AI Hardware Worth the Cost?

Cloud AI subscriptions typically run $20 to $200 a month, depending on usage, and that cost continues indefinitely. Buying or already owning capable hardware is a one-time cost, and once you have it, there's no per-message fee, no rate limit, and no dependence on an internet connection. For anyone using AI daily, the break-even point compared to an ongoing subscription tends to arrive faster than people expect. The other side of that equation is privacy: nothing you type ever leaves your device.

Apple Silicon vs a Traditional PC Setup

The core difference comes down to memory architecture. On a Windows PC, your system RAM and your GPU's VRAM are two separate pools, and a model has to fit within whichever pool is smaller. That usually means the GPU, since consumer graphics cards top out well below what system RAM can offer.

Apple Silicon removes that ceiling by sharing one memory pool across everything. It's a big part of why Macs have become such a practical choice for local AI, letting you run larger, more capable models without needing a specialized graphics card at all. If you're weighing a hardware upgrade for AI work, unified memory capacity is the number to focus on before anything else.

Frequently Asked Questions

What hardware do I need to run AI locally?

At a minimum, 16GB of memory and a modern processor from the last few years. On a PC, a GPU with at least 8GB of VRAM makes a real difference. On a Mac, unified memory covers both roles, so 16GB is enough for 7B to 8B models.

How much RAM is needed for local AI?

16GB is the practical minimum for models that feel genuinely useful. 8GB works for smaller models, but you'll be limited to the 1B–3B range. 32GB or more opens up noticeably better models.

What hardware is required for AI in general, beyond just running models?

Running models locally only needs enough memory to hold the model and a capable CPU or GPU. Training or fine-tuning models is a different task entirely and needs far more memory and dedicated GPU hardware than everyday inference.

Does local AI need a dedicated GPU?

Not on a Mac. Apple Silicon's unified memory means the built-in GPU handles inference without a separate graphics card. On Windows, a discrete GPU with enough VRAM makes a real difference in speed.

How much storage do local AI models take up?

Individual models range from 1GB to 40GB or more. Plan for at least 100GB free if you want to try several models.

Does CPU speed matter for running AI locally?

It affects how quickly a model loads and how responsive the interface feels, but memory capacity has a far bigger impact on what you can run at all.

Choosing the Right Setup, Without the Guesswork

Memory is the first thing to check, whether that's RAM, VRAM, or unified memory on a Mac. Once you know that ceiling, everything else, CPU generation, GPU model, storage speed, just shapes how smooth the experience feels around the edges.

For most people, 16GB is where local AI starts feeling genuinely useful, 32GB is where it gets comfortable, and 64GB is where you stop thinking about model size altogether. If you're buying or upgrading hardware specifically for this, memory capacity is worth prioritizing over every other spec on the list.

Once your setup is sorted, the next step is picking a model that actually fits it. This walkthrough on running AI models locally on your Mac covers model formats, quantization, and how to get chatting in a few minutes with Lekh AI, without digging through terminal commands or config files.