How to Run AI on Your Own PC in 2026: The Complete Local LLM Hardware Guide

A couple of years ago, if you wanted to use a capable AI chatbot, you went to a website, typed your question, and your words travelled to a data centre somewhere on the other side of the world. That’s still how most people use AI. But something quietly changed over the last eighteen months. The open models you can download and run on your own computer got good. Not “fun toy” good, but genuinely useful for writing, summarising, coding, translating and answering questions, all without an internet connection and without sending a single word to anyone else’s server.

In 2026 this has turned into one of the most talked about topics in PC hardware. Google released its Gemma 4 family of open models in April, under the permissive Apache 2.0 licence. OpenAI, which for years kept its models locked behind an API, published open-weight models called gpt-oss that are designed to run on a single high-end PC. Alibaba’s Qwen models, Meta’s Llama family, Mistral and DeepSeek have all pushed out new releases. At the same time, hardware makers have started designing products specifically around running AI at home, from AMD’s Ryzen AI Max chips with up to 128GB of shared memory to Nvidia’s small DGX Spark box.

We get asked about this more and more at the shop. “Can my PC run ChatGPT offline?” “How much VRAM do I need for AI?” “Is it worth buying a Mac for this?” “Will an RTX 5060 do?” The honest answers are more interesting than a simple yes or no, and they depend a lot on what you actually want to do.

This guide covers it all. What a local AI model actually is, why people want one, how models are measured and squeezed down to fit, how much memory you really need, which graphics cards and other hardware make sense at every budget, which software to use, which models to start with, and how to set everything up step by step. We’ve tried to keep it plain English throughout, and where we quote numbers from other people’s testing, we’ve linked the sources at the end.

One quick warning before we start. This field moves very fast. New models come out almost every week, and the “best” model today might be replaced next month. The principles in this guide, about memory, speed and hardware, change much more slowly, so focus on those and you won’t go far wrong.

The short version, if you’re in a hurry

What it is: Running an AI language model directly on your own PC, instead of using a website. Tools like Ollama and LM Studio make this almost as easy as installing any other app.
Why do it: Privacy, no monthly subscription, works offline, no usage limits, and you can connect it to your own files and tools.
The catch: Local models you can run on a normal PC are generally less capable than the biggest cloud models. The gap has shrunk a lot, but it hasn’t disappeared.
The number that matters most: memory. For a graphics card, that’s VRAM. The model needs to fit in your GPU’s memory to run fast. When it doesn’t fit, speed falls off a cliff.
Rough guide: 8GB of VRAM runs small models (around 4 to 8 billion parameters) well. 12 to 16GB runs mid-sized models comfortably, including gpt-oss-20b. 24 to 32GB opens up much stronger models. For the very largest open models you need 64 to 128GB or more of memory, which is where Macs with lots of unified memory and AMD’s Ryzen AI Max machines come in.
Best value starting point: a graphics card with 16GB of VRAM, such as an RTX 5060 Ti 16GB, plus 32GB of system RAM and a fast 1TB or bigger NVMe SSD.
Good first models: Gemma 4, Qwen3, gpt-oss-20b and Llama models, in sizes that match your memory.

What’s in this guide

What “running AI locally” actually means
Why people are doing it, and when the cloud is still better
Parameters, quantization and why memory is everything
How fast is fast enough? Tokens, prompts and context
Hardware tiers, from budget to extreme
Nvidia, AMD, Intel and Apple compared
The rest of the PC: RAM, storage, CPU, power and cooling
The software: Ollama, LM Studio, llama.cpp and friends
Which models to start with
Step-by-step setup
Useful things to actually do with it
Privacy, safety and licences
Myths, mistakes and troubleshooting
Frequently asked questions

Part 1: What “running AI locally” actually means

When you use a cloud AI service, three things happen. You type a message. That message is sent over the internet to the company’s servers. A very large AI model running on very expensive hardware in a data centre generates a reply, and the reply is sent back to you. Everything clever happens far away from your computer.

Running AI locally means the model itself lives on your PC. You download a file, often several gigabytes, sometimes tens of gigabytes, that contains the trained model. A program on your PC loads that file into memory and uses your hardware, mostly your graphics card, to generate the replies. Your messages never leave your machine. If you unplug the internet cable, it keeps working.

What a language model actually is

A large language model, or LLM, is a type of AI that has been trained on enormous amounts of text to predict what word, or more precisely what piece of a word, comes next. That sounds simple, almost silly, but when the model is large enough and trained well enough, predicting the next word turns out to require a lot of what looks like understanding. To continue a sentence about a recipe sensibly, the model has to have learned something about cooking. To finish a line of Python code, it needs to have learned something about programming.

The “knowledge” of the model is stored in its weights, also called parameters. These are simply numbers, billions of them, that were adjusted during training. When people say a model is “7B” or “70B,” they mean it has about 7 billion or 70 billion of these numbers. Generally, more parameters means a more capable model, although newer, better trained small models regularly beat older, bigger ones.

What “open weights” means

Companies like OpenAI, Google and Anthropic have historically kept their most powerful models private. You can use them, but you can’t download them. An open-weight model is one where the company publishes the weights file so anyone can download it and run it themselves. That’s what makes local AI possible.

You’ll often hear these called “open source” models. That’s not always accurate. True open source would mean publishing the training data and code too, which most companies don’t do. And the licences vary a lot. Some, like Apache 2.0 or MIT, let you do almost anything including commercial use. Others, like Meta’s Llama community licence, come with conditions. We’ll come back to licences in Part 12, because if you’re using a model for a business, it matters.

Training vs running

It’s worth being clear on one point that confuses a lot of people. Training a large model from scratch costs millions of dollars and needs thousands of data centre GPUs running for weeks or months. You’re not going to do that at home, and you don’t need to. Running a model that someone else has already trained, which is called inference, needs far less power. That’s what this guide is about.

There’s a middle ground called fine-tuning, where you take an existing model and train it a little more on your own data. Light fine-tuning of small models is possible on a good home PC, but it’s an advanced topic and most people get what they need without it.

It’s not just chatbots

Language models get most of the attention, but you can run lots of other types of AI locally too. Image generators like Stable Diffusion and Flux create pictures from text descriptions. Speech recognition models like Whisper turn audio into text, which is brilliant for transcribing meetings or lectures. Text-to-speech models read text out loud in natural sounding voices. Vision models can describe images or read text from a photo. We’ll focus mainly on language models in this guide, since that’s what most people mean, but the hardware advice applies to all of them, and memory is king in every case.

Part 2: Why people are doing it, and when the cloud is still better

If cloud AI is so good and often free to try, why would anyone bother running it themselves? There are several real reasons, and a few that are more about curiosity. Both are fine.

Privacy

This is the big one. When you paste a contract, a medical report, a business plan, customer data or your private diary into a cloud chatbot, you’re trusting that company with it. Most reputable providers have privacy policies, and many offer business plans that promise not to train on your data. But your information still leaves your control. For lawyers, doctors, accountants, businesses with confidential data, or anyone who simply doesn’t like the idea, a local model removes the question completely. The data never leaves the room.

We’ve seen this become especially important for small businesses here in Pakistan that handle client documents. Being able to summarise or draft from those documents without uploading them anywhere is a genuine advantage.

No subscription, no limits

The best cloud AI plans cost money every month, and even paid plans have usage limits on the most capable models. Developers using AI through an API pay per word, and those costs add up fast if you’re processing lots of documents. A local model costs nothing per use once you have the hardware. Run it all day, process a thousand documents, nobody sends you a bill. The only ongoing cost is electricity.

That said, be honest with yourself about the maths. If you’d only use AI for a few questions a week, a free cloud tier or a cheap subscription will be far more economical than buying a new graphics card. Local AI makes the most financial sense if you use it heavily, or if you were buying a capable PC anyway.

Offline and reliable

A local model works without internet. That’s useful when travelling, in areas with poor connectivity, during outages, or simply when a cloud service is down or overloaded, which does happen. With load shedding and internet disruptions still a reality for many people, having a capable assistant that runs on your own machine (and your own UPS) is more practical than it might sound.

Control and customisation

With a local model you choose exactly which model and version you use, and it never changes without your permission. Cloud services update their models regularly, which can change how they respond overnight, sometimes for the better, sometimes not. Locally, if a model works well for your task, you can keep using that exact model for years.

You can also connect local models to your own documents, notes and tools, set custom instructions, and choose models with different personalities or specialities. Developers can build apps and automations on top of a local model without worrying about API costs or rate limits.

Learning

Plenty of people do it simply because it’s interesting. Running models yourself teaches you a lot about how AI actually works, what it’s good at, and where it falls down. For students and anyone in tech, that understanding is increasingly valuable.

When the cloud is still better

We want to be straight about this. The largest cloud models from the major AI labs are still more capable than anything most people can run on a home PC, especially for the hardest tasks: complex reasoning, tricky coding problems, long multi-step research, and up-to-date information from the web. The very best open models are competitive with cloud models, but those are often hundreds of billions or even trillions of parameters in size, and need server hardware to run at full quality.

Cloud services also come with conveniences that take effort to replicate locally, like web search, polished mobile apps, voice modes and file handling built in.

For many people, the best answer is both. Use a local model for private documents, everyday drafting, quick questions and heavy repeated tasks, and use a cloud service when you need the absolute best answer to a hard problem. There’s no rule that says you must pick one.

Part 3: Parameters, quantization and why memory is everything

If you only remember one thing from this guide, make it this: for running AI locally, memory capacity matters more than almost anything else. Not CPU speed, not the number of cores, not even raw GPU power, at least not first. Let’s see why.

How big is a model, really?

A model’s weights have to be loaded into memory before it can generate anything. Each weight is a number, and each number takes up space. In the format models are usually trained in, called 16-bit (FP16 or BF16), each parameter takes 2 bytes. So a quick rule of thumb is:

A 7 billion parameter model at 16-bit needs about 14GB just for the weights.
A 14 billion parameter model needs about 28GB.
A 70 billion parameter model needs about 140GB.

That would put even mid-sized models out of reach for nearly every home PC. Fortunately there’s a trick that changes everything.

Quantization, explained simply

Quantization means storing each weight with fewer bits, so it takes less space. Instead of 16 bits per weight, you might use 8 bits, or 4 bits, or even less. A 4-bit version of a model is roughly a quarter the size of the 16-bit original.

Think of it like saving a photo as a JPEG. The original might be a huge, perfect file. A high-quality JPEG is much smaller and looks almost identical. A very compressed JPEG is tiny but you start seeing blocky artifacts. Quantization works similarly. A well-made 8-bit version is very hard to tell apart from the original. A 4-bit version is usually a little worse, but often only slightly, and for most everyday use you’d struggle to notice. Go down to 3 bits or 2 bits and quality starts to drop more noticeably, especially for small models.

With 4-bit quantization, the earlier numbers look much friendlier. Using the common estimate from local AI guides:

A 7 to 8B model at 4-bit needs roughly 4 to 5GB.
A 14B model needs roughly 8 to 10GB.
A 30B-class model needs roughly 16 to 20GB.
A 70B model needs roughly 40GB.

That’s why 4-bit quantization has become the default for local AI. It’s the sweet spot where models get small enough to fit on normal hardware while keeping most of their quality.

The alphabet soup: Q4_K_M, GGUF, MXFP4 and friends

When you go to download a model, you’ll see names like Q4_K_M, Q5_K_S, Q8_0 and file types like GGUF. Don’t panic. Here’s the practical version:

GGUF is a file format used by llama.cpp and the tools built on top of it, including Ollama and LM Studio. It’s the most common format for local models on PCs.
The number after Q is roughly the bits per weight. Q4 is about 4-bit, Q5 is about 5-bit, Q8 is about 8-bit.
The letters after it (K_M, K_S and so on) describe the specific method. K_M (“medium”) is a very popular balance of size and quality.
MXFP4 and NVFP4 are newer 4-bit formats designed with hardware support in mind. OpenAI’s gpt-oss models were released using MXFP4, which is a big part of why the 20B version fits on 16GB machines.
MLX is Apple’s framework for running models efficiently on Apple Silicon Macs, with its own model versions.

If you’re not sure which to pick, Q4_K_M is a sensible default for most models. If you have memory to spare, Q5 or Q6 gives a little more quality. Q8 is near-lossless but twice the size of Q4.

Memory isn’t just for the weights

Here’s something many first-timers miss. The model weights aren’t the only thing that needs memory. While the model is generating, it keeps a running memory of the conversation, called the KV cache. The longer your conversation or document, the bigger this cache gets. With a long context, say pasting in a 50-page document, the KV cache can need several extra gigabytes on top of the model itself.

So if a model file is 9GB, don’t assume it’ll run comfortably on a 10GB graphics card. You need headroom for context, plus a little for your operating system and display if the GPU is also running your monitor. As a rough habit, aim for a model file that’s at least 1.5 to 2GB smaller than your VRAM for short chats, and more if you want long documents.

Why VRAM specifically?

Your graphics card has its own memory, VRAM, which is extremely fast. A modern graphics card can read its VRAM at hundreds of gigabytes per second, and high-end cards at well over a terabyte per second. Normal system RAM in a desktop PC is far slower, typically somewhere around 60 to 100 gigabytes per second with dual-channel DDR5.

That speed matters because, to generate each word, the model has to read through essentially all of its active weights. If those weights are sitting in fast VRAM, generation is quick. If some of them have to be read from slower system RAM, everything slows to the pace of the slowest part.

Tools like Ollama and LM Studio can split a model, putting some layers on the GPU and the rest in system RAM. This works, and it means you can technically run models bigger than your VRAM. But as one widely shared guide puts it, the single biggest performance cliff in local LLM inference is the moment your model spills from GPU to CPU. A model that runs at 40 tokens per second fully on the GPU might drop to 5 or 10 tokens per second once a big chunk of it lands in system RAM. Usable for some things, frustrating for chat.

This is why, for local AI, a graphics card with more VRAM but less raw power is often a better buy than a faster card with less VRAM. A 16GB mid-range card will run a 14B model much better than a more expensive 8GB or 12GB card that can’t fit it.

Mixture of Experts: big models that act small

There’s one more twist that’s become very important in 2026. Many new models use an architecture called Mixture of Experts, or MoE. Instead of using every parameter for every word, an MoE model has many “expert” sections and only activates a few of them for each word it generates.

Model names often reflect this. Gemma 4’s “26B A4B” model, for example, has about 25 billion parameters in total, but only around 4 billion are active for each token, according to Google’s documentation. OpenAI’s gpt-oss-20b has around 21 billion parameters, but only about 3.6 billion active per token.

Why does this matter for your hardware? Two reasons:

Speed depends mostly on the active parameters. Since only a fraction of the model is read for each word, MoE models generate much faster than a normal (“dense”) model of the same total size.
Memory still depends on the total parameters. All the experts still need to be loaded somewhere, so you need enough memory for the whole thing.

This makes MoE models a great fit for systems with lots of reasonably fast memory, like Apple Macs with large unified memory or AMD’s Ryzen AI Max machines. It also makes partial offloading less painful than with dense models, because the slower system RAM only has to serve a small number of active experts per word. Some people get surprisingly usable speeds running big MoE models with the attention layers on the GPU and the experts in system RAM.

Part 4: How fast is fast enough? Tokens, prompts and context

Speed in local AI is measured in tokens per second. A token is a chunk of text, often a whole short word, sometimes part of a longer word. As a rough guide, 100 tokens is about 75 English words. Urdu and other languages often use more tokens per word, so they run a bit slower in words per second.

Two kinds of speed

There are actually two very different speeds to think about:

Prompt processing (prefill): how fast the model reads your input. If you paste in a long document, the model has to process all of it before it starts replying. This stage is heavy on raw compute, and graphics cards with lots of processing power, especially Nvidia cards with Tensor Cores, are very fast at it.
Generation (decode): how fast the model writes its reply, one token after another. This stage is mostly limited by memory bandwidth, how quickly the hardware can read the model’s weights.

That distinction explains a lot of the confusing benchmark results you’ll see online. A machine with lots of memory but modest compute, like some unified memory systems, might generate text at a perfectly pleasant speed but take a long time to “read” a big document before replying. A powerful graphics card might chew through the document in seconds.

One independent comparison of Nvidia’s DGX Spark and an AMD Ryzen AI Max+ 395 machine, both with 128GB of memory, found them close in generation speed on a 120B-class model, roughly 38 tokens per second versus 34, but the Nvidia system was decisively faster at prompt processing. If most of your use involves feeding in long documents, that matters more than the generation number.

What feels fast?

Here’s how different generation speeds feel in practice, from our own experience:

Under 5 tokens per second: slow. You’ll watch the words appear one by one. Fine for leaving a long task running, painful for chat.
5 to 10 tokens per second: roughly reading speed for many people. Usable, a bit sluggish.
10 to 20 tokens per second: comfortable for chat. Text appears faster than most people read.
20 to 50 tokens per second: feels quick and responsive.
50+ tokens per second: very fast. Mostly useful when generating long outputs, coding, or when the model “thinks” before answering.

That last point matters more in 2026 than it used to. Many of the newer models are reasoning models that write out a long chain of thinking before giving their final answer. That thinking can be hundreds or thousands of tokens. At 10 tokens per second, a reasoning model might spend a minute or two thinking before it answers. At 50 tokens per second, the same answer arrives in seconds. So if you plan to use reasoning models a lot, speed matters more.

Context length

Context length, or context window, is how much text the model can consider at once: your instructions, the conversation so far, and any documents you’ve given it. Modern models advertise large context windows. Gemma 4’s larger models support up to 256K tokens according to Google, and some open models claim a million tokens or more.

But here’s the practical reality: on a home PC, the context you can actually use is limited by memory, because the KV cache grows with context. Many local tools set a modest default context, often a few thousand tokens, to save memory. If you paste a long document and the model seems to “forget” the beginning, the context setting is probably too small. You can raise it, but each increase costs memory and slows things down a little. For most chat and short document work, 8K to 32K tokens of context is plenty.

Part 5: Hardware tiers, from budget to extreme

Now for the part most people actually came for: what hardware do you need? We’ve split this into tiers by memory, because that’s the thing that decides which models you can run. Within each tier, a faster card will generate faster, but the memory decides what’s possible at all.

A quick note before we start. We’re deliberately not quoting rupee prices here. With the memory shortage pushing up graphics card and RAM prices this year, and exchange rates moving around, any number we print would be out of date in weeks. Check our current listings or ask us for an up-to-date quote.

Tier 0: No dedicated GPU (CPU and integrated graphics)

Can you run AI with no graphics card at all? Yes, small models will run on a modern CPU using system RAM. A recent 6 or 8 core processor with 16 to 32GB of fast DDR5 can run a 3 to 4B model at a usable speed, and even a 7 to 8B model at a few tokens per second. Integrated graphics on recent AMD and Intel chips can help a little with some tools.

Good for: trying local AI out, light summarising, simple questions, transcription with small Whisper models. Not good for: anything where you want quick, high-quality answers. Treat this as a taster, not a long-term setup.

Tier 1: 8GB of VRAM

Cards like the RTX 5050, RTX 5060, the 8GB RTX 5060 Ti, RX 9060 XT 8GB, and older cards like the RTX 3060 Ti, 4060 and 3070.

With 8GB you can comfortably run models up to about 7 to 8 billion parameters at 4-bit, and small MoE models, with room for a reasonable context. Generation is fast on these, often 40+ tokens per second on modern cards. You can also squeeze in some 12 to 14B models at lower quantization, but it’ll be tight and context will be limited.

Good for: everyday chat, drafting emails and messages, simple coding help, translation, summarising short documents, image generation with smaller image models. Small models in 2026 are dramatically better than they were two years ago, so this tier is genuinely useful. Limitations: you’ll notice the smaller models make more factual mistakes and struggle with complex reasoning.

Tier 2: 12 to 16GB of VRAM (the sweet spot)

Cards like the RTX 5070 (12GB), RTX 5060 Ti 16GB, RTX 5070 Ti and RTX 5080 (16GB), RX 9060 XT 16GB, RX 9070 and 9070 XT (16GB), Intel Arc B580 (12GB), and previous generation 16GB cards.

This is where local AI goes from “neat” to “properly useful.” With 16GB you can run 12 to 14B dense models at good quality with plenty of context, mid-sized MoE models like Gemma 4 26B A4B at 4-bit, and OpenAI’s gpt-oss-20b, which was specifically designed to run on 16GB devices. These models are noticeably smarter, better at following detailed instructions, and much better at coding than the small ones.

If you’re buying a card with AI in mind, we think a 16GB card is the best value starting point for most people. The RTX 5060 Ti 16GB in particular is often a much better buy for AI than a 12GB RTX 5070, even though the 5070 is faster in other workloads, simply because those extra 4GB let you run bigger models fully on the GPU.

Tier 3: 24 to 32GB of VRAM

The RTX 5090 (32GB), the previous RTX 4090 and 3090 (24GB), and some workstation cards like Intel’s Arc Pro B60 with 24GB.

Here you can run 27 to 32B dense models at good quality, which many people consider the best balance of intelligence and speed you can get on a single consumer card. Gemma 4 31B, Qwen3 32B-class models and similar fit nicely at 4-bit, with room for a decent context. You can also run larger MoE models with partial offloading at usable speeds.

The RTX 5090 is the fastest consumer card you can buy for this, with 32GB of very fast memory. It’s also very expensive, draws up to around 575 watts, and needs a serious power supply. A used RTX 3090 has become a famous “budget 24GB” choice among local AI enthusiasts worldwide because of its memory. It’s an older, power hungry card, so check condition and warranty carefully if you go that route.

Good for: serious coding assistance, complex writing, research and analysis, reasoning models at good speed, larger image and video models.

Tier 4: Multiple GPUs

Some enthusiasts put two or more graphics cards in one PC. Tools like llama.cpp, Ollama and vLLM can split a model across cards, so two 16GB cards give you about 32GB to work with, and two 24GB cards give you 48GB, enough for 70B models at 4-bit.

It works, but it’s not as simple as it sounds. You need a motherboard with enough properly spaced PCIe slots, a case with room and airflow for two big cards, and a power supply that can handle both. Each card still only uses its own memory and bandwidth, so two cards won’t generate twice as fast for one conversation, they just let you run bigger models. If you’re considering this, talk to us about the build first, because getting the motherboard, case and PSU right is most of the battle.

Tier 5: Big unified memory (64 to 128GB and beyond)

This is the category that has really exploded in 2026. Instead of separate system RAM and VRAM, some systems give the GPU access to a large pool of shared memory:

Apple Silicon Macs with M-series Max and Ultra chips, available with 64GB, 128GB, and on the Mac Studio, even more unified memory.
AMD Ryzen AI Max+ 395 (“Strix Halo”) mini PCs and laptops, with up to 128GB of shared LPDDR5X memory, a large chunk of which can be assigned to the built-in graphics.
Nvidia DGX Spark and similar boxes from partners, built around Nvidia’s GB10 chip with 128GB of unified memory.

These can load models that no single consumer graphics card can: 70B dense models, and large MoE models with over 100 billion parameters, such as gpt-oss-120b. One report found a Ryzen AI Max machine with 128GB running Qwen3-235B, a model with about 22 billion active parameters, at around 11 tokens per second. That’s remarkable for a box that fits on a desk.

The trade-off is speed per token on dense models. The memory in these systems is fast, but slower than the GDDR7 on a top graphics card, so a model that fits on an RTX 5090 will usually run faster there. And as we mentioned, prompt processing can be much slower on some of them. They shine with large MoE models, where their big memory matters most and only a small fraction of the weights is read for each word.

Price-wise, one comparison listed the Ryzen AI Max+ 395 systems at roughly half the price of the DGX Spark, both with 128GB, with similar generation speed but the Nvidia box much faster for prompt processing and better supported by Nvidia’s software stack. AMD’s own marketing claims better tokens per second per dollar, which as always you should treat as a vendor claim.

Part 6: Nvidia, AMD, Intel and Apple compared

Beyond memory, the brand of hardware you choose affects how easy it is to get things working, which software you can use, and how fast it runs.

Nvidia: the default for a reason

Nvidia’s CUDA software platform has been the standard for AI for over a decade. Almost every AI tool, model and tutorial assumes an Nvidia card first. If something new comes out, it works on Nvidia first, often on day one. RTX cards also have Tensor Cores, dedicated hardware for AI maths, which make them very fast at prompt processing, and the RTX 50 series adds support for 4-bit FP4 formats in hardware.

The downside is price and, historically, VRAM. Nvidia has been criticised for putting 8GB on cards where competitors offer 16GB. Their 16GB options are the ones to look at for AI.

Verdict: the easiest, most compatible choice. If you want things to just work, especially for image generation and less common tools, Nvidia is the safe bet.

AMD: much better than it used to be

AMD’s equivalent of CUDA is called ROCm. A few years ago, running AI on AMD cards on Windows was a headache. That’s improved hugely. LM Studio and Ollama both support AMD graphics cards, llama.cpp has a Vulkan backend that runs well on AMD hardware, and AMD has been actively working with these projects. AMD cards also often give you more VRAM for the money, like 16GB on the RX 9060 XT and RX 9070 series.

Some cutting-edge tools and image generation workflows still work better or are easier to set up on Nvidia. For mainstream language model use with the popular apps, AMD is now a very reasonable choice.

Verdict: good value, especially for VRAM per rupee. Check that the specific tools you want to use support your card before buying.

Intel: interesting budget option

Intel’s Arc graphics cards, like the B580 with 12GB, offer a lot of memory for the price. Intel has its own AI software (OpenVINO and IPEX-LLM) and support in llama.cpp through SYCL and Vulkan. Compatibility is improving but is still behind Nvidia and AMD, so expect a bit more tinkering.

Verdict: for budget builders who don’t mind experimenting.

Apple: the big memory option

Apple’s unified memory design is very well suited to running large models. A Mac with lots of memory can run models that would need several graphics cards on a PC, quietly and with low power use. Apple’s MLX framework and tools like LM Studio and Ollama support Macs very well.

The downsides: Macs with large memory configurations are very expensive, memory can’t be upgraded later, prompt processing is slower than on a high-end Nvidia card, and some AI tools, particularly in image generation, are Nvidia-first. For people who already use a Mac and want to run big models, though, it’s an excellent option.

NPUs: what about the “AI PC” chip?

Many new laptops and some desktop processors include an NPU, a neural processing unit, and are marketed as “AI PCs.” It’s natural to assume the NPU is what you’d use for local AI. In practice, for running language models with popular tools, the graphics card or GPU does most of the heavy lifting today. NPUs are designed for efficient, low-power AI features like background blur, live captions and some built-in Windows features. Support for running full chatbots on NPUs is growing, but it’s not where the performance is yet. We’ll look at this properly in a later article in this series about whether AI PCs are worth paying extra for.

Part 7: The rest of the PC: RAM, storage, CPU, power and cooling

The graphics card gets the headlines, but the other parts of the PC still matter, and getting them wrong can hold back an otherwise good setup.

System RAM

Even if your model runs entirely on the GPU, the model file is usually loaded through system RAM first, and your other apps need memory too. If you plan to offload part of a model to system RAM, then RAM becomes very important.

16GB: the bare minimum. Fine if your model fits entirely in VRAM and you don’t run much else.
32GB: our recommendation for most people doing local AI. Comfortable headroom, and lets you offload a little when needed.
64GB or more: worth it if you want to run large MoE models with experts offloaded to RAM, or keep several models loaded at once.

Speed matters for offloading. Dual-channel DDR5 is much faster than DDR4, and running two sticks (rather than one) is essential to get dual-channel bandwidth. If you do lots of offloading, faster DDR5 kits help a little more. Note that as we covered in our article on the 2026 memory crisis, RAM prices have climbed sharply this year, so plan your budget accordingly and buy the capacity you’ll actually use.

Storage

Model files are big. A 4-bit 8B model is around 5GB, a 32B model about 20GB, and a 70B model about 40GB. Once you start experimenting, it’s very easy to end up with a few hundred gigabytes of models. We’d suggest:

A 1TB NVMe SSD minimum, ideally 2TB if you plan to try lots of models or do image generation.
A fast NVMe drive makes loading models much quicker. A big model can take a long time to load from a slow hard drive, while a good Gen4 NVMe will load it in seconds.
Keep your models on an SSD, not a mechanical hard drive.

CPU

If the model runs on the GPU, the CPU isn’t doing much. Any modern 6-core or better processor is fine. The CPU matters more if you plan to run models on the CPU or offload large parts of them, in which case more cores and support for fast memory helps. For a pure GPU setup, don’t overspend on the CPU and put the money into VRAM instead.

Motherboard

For a single graphics card, any decent motherboard with a PCIe x16 slot works. For multiple graphics cards, you need a board with suitable slot spacing and enough PCIe lanes, which usually means higher-end boards. Check this carefully before buying a second card.

Power supply

Running a model keeps your graphics card working hard, sometimes for long periods if you’re processing lots of documents. Make sure your power supply is a quality unit with enough capacity for your card’s maximum power, plus your CPU and headroom. For an RTX 5090, Nvidia recommends a 1000W power supply. For mid-range cards, a good 650 to 750W unit is usually plenty. As always, a reliable brand matters more than a big number on the label.

With load shedding and voltage swings, a UPS is a sensible addition for a machine you rely on. Check the UPS can actually handle your system’s peak power, not just its idle draw.

Cooling and noise

Long AI jobs can keep your GPU near full load for hours. Make sure your case has good airflow, and clean dust filters regularly, especially in our hot summers. If the PC lives in a bedroom or office, consider the noise too. Some people limit their GPU’s power slightly (power limiting or undervolting), which often reduces heat and noise significantly with only a small drop in AI speed, since generation is limited more by memory than by raw compute.

Laptops for local AI

Any laptop with a decent dedicated Nvidia or AMD GPU can run local models. The same rule applies: VRAM decides what fits. Laptop GPUs often have less VRAM than desktop cards with similar names, so check the exact specs. Laptops with AMD’s Ryzen AI Max chips and lots of shared memory are an interesting option for running larger models on the go. And of course, MacBook Pros with lots of unified memory are popular for this. Just remember laptops run hotter and throttle under long loads.

Part 8: The software: Ollama, LM Studio, llama.cpp and friends

The good news is you don’t need to be a programmer to run local AI anymore. Here are the main tools, from easiest to most advanced.

LM Studio: the easiest place to start

LM Studio is a desktop app for Windows, Mac and Linux that makes local AI feel like any other program. It has a built-in model browser where you can search for models, see which versions will fit on your hardware, and download them with one click. Then you chat with them in a clean interface that will feel familiar if you’ve used any cloud chatbot.

It also has a local server mode, so other apps on your PC can use the model as if it were an online AI service. It supports Nvidia, AMD and Intel graphics, and Apple Silicon, including MLX models on Macs.

Best for: beginners, writers, researchers, anyone who wants a graphical app and doesn’t want to touch a command line.

Ollama: simple and powerful

Ollama is probably the most popular tool for running local models. It started as a command line tool, where you type something like ollama run gemma4 and it downloads and runs the model. It now also has a desktop app with a chat window on Windows and Mac. Its big strength is how easy it makes using local models from other software. Lots of apps, plugins and coding tools can connect to Ollama directly.

Best for: people who want a simple, reliable engine running in the background, developers, and anyone who wants to connect local AI to other apps.

llama.cpp: the engine underneath

llama.cpp is the open source project that powers a lot of local AI, including parts of Ollama and LM Studio. It’s very fast, supports a huge range of hardware (Nvidia, AMD, Intel, Apple and plain CPUs), and gives you fine control over every setting. It’s command line based and more technical, but if you want maximum performance or the latest features first, this is it.

Best for: enthusiasts and advanced users.

Open WebUI and other chat interfaces

Open WebUI is a popular open source chat interface that runs in your web browser and connects to Ollama or other backends. It looks and feels a lot like the big cloud chatbots, with conversation history, document uploads, multiple users and more. It’s a nice option if you want to set up local AI for a whole household or small office, with the model running on one powerful PC and everyone else connecting over the local network.

vLLM and other server tools

vLLM is designed for serving models to many users at once, efficiently. It’s what you’d use if you were building a service for a team or a business on a powerful server. It’s more complex to set up and works best on Linux with Nvidia GPUs. Most home users won’t need it.

Tools for other kinds of AI

ComfyUI is the most popular tool for local image generation with models like Stable Diffusion and Flux. It uses a visual node-based interface, very powerful, with a learning curve.
Whisper (and faster versions of it) handles speech to text. Various apps wrap it in a simple interface for transcribing audio files.
Coding assistants in editors like VS Code can connect to local models through Ollama or LM Studio, giving you code completion and chat without sending your code to the cloud.

Which should you pick?

If you’re brand new: start with LM Studio. If you’re comfortable with a terminal or want to connect AI to other tools: Ollama. If you want a shared, browser-based chat for your home or office: Ollama plus Open WebUI. You can always try more than one, they’re all free.

Part 9: Which models to start with

There are thousands of models available on sites like Hugging Face, which can be overwhelming. Here are the families we’d look at first in late 2026. Remember, new versions come out constantly, so check for the latest release in each family.

Gemma 4 (Google)

Released in April 2026 under the Apache 2.0 licence, Gemma 4 comes in several sizes: E2B and E4B (small “effective parameter” models designed for phones and light hardware), a 12B model added in June, the 26B A4B mixture-of-experts model, and the 31B dense model. According to Google, the larger models support context windows up to 256K tokens. Several 2026 guides rate Gemma 4 as one of the best choices for quality on a consumer GPU.

Try: E4B on 8GB cards, 12B or 26B A4B on 16GB cards, 31B on 24 to 32GB.

gpt-oss (OpenAI)

OpenAI’s open-weight models, released under the Apache 2.0 licence. gpt-oss-20b has about 21 billion total parameters with about 3.6 billion active, and was released in the MXFP4 4-bit format so it runs on devices with 16GB of memory. gpt-oss-120b is the larger version, designed to run on a single 80GB data centre GPU, or on 128GB unified memory systems at home. Both are reasoning models, strong at logic and step-by-step problems for their size.

Try: gpt-oss-20b on a 16GB card. It’s one of the best things you can run on mid-range hardware.

Qwen (Alibaba)

The Qwen family from Alibaba has been one of the most popular open model families for a while, with a wide range of sizes from tiny to huge, and strong multilingual and coding abilities. Qwen3 models, including MoE versions, are widely recommended for general use. There are dedicated coding versions too.

Try: a small Qwen3 model on 8GB, the mid-size models on 16 to 24GB, and the large MoE versions on 128GB unified memory systems.

Llama (Meta)

Meta’s Llama models kicked off the open model movement. Llama 4 models like Scout use a mixture-of-experts design and offer very long context windows. Note that Llama uses Meta’s own community licence, which has some conditions, rather than a fully permissive open source licence.

Mistral, DeepSeek and others

Mistral, a French company, makes efficient models in various sizes, many under Apache 2.0. DeepSeek’s models gained huge attention for strong reasoning at low cost. The very largest models from DeepSeek, Moonshot (Kimi), Z.ai (GLM) and Alibaba are among the most capable open models in the world, but at hundreds of billions to trillions of parameters, they need server hardware to run at full quality. Smaller “distilled” versions of some of these exist, which bring some of their abilities down to sizes you can run at home.

Specialist models

Coding models are tuned specifically for programming. If coding is your main use, look for the coding versions of the families above.
Vision models can understand images. Several Gemma 4 and Qwen models handle images as well as text.
Embedding models are small models that turn text into numbers for search. They’re used behind the scenes when you build a system to search your own documents.

What about Urdu?

Good question, and one we get asked. Multilingual support has improved a lot, and the larger models in the Qwen, Gemma and Llama families handle Urdu reasonably, though usually less well than English, and small models struggle more. Roman Urdu (Urdu written in English letters) tends to work surprisingly well with bigger models. If Urdu is important to you, test a few models with your own typical prompts before committing to one, and lean towards larger models where your hardware allows.

A quick sizing cheat sheet

8GB VRAM: 4 to 8B models at Q4 to Q6, Gemma 4 E4B, small Qwen3 models.
12GB VRAM: up to about 12 to 14B at Q4, gpt-oss-20b with some care.
16GB VRAM: 14B at higher quality, gpt-oss-20b, Gemma 4 12B or 26B A4B at Q4.
24 to 32GB VRAM: 27 to 32B dense models at Q4 to Q6, Gemma 4 31B.
64GB unified: 70B dense at Q4 (slower), large MoE models.
128GB unified: gpt-oss-120b, large Qwen3 MoE models, 70B at higher quality.

Part 10: Step-by-step setup

Let’s actually get something running. We’ll walk through the two easiest routes, LM Studio and Ollama, on a Windows PC. The process is very similar on Mac and Linux.

Before you start

Update your graphics driver. For Nvidia, use the Nvidia app or download the latest driver from Nvidia’s website. For AMD, use AMD Software: Adrenalin Edition. Old drivers are the number one cause of “it’s running on my CPU instead of my GPU” problems.
Check your VRAM. Open Task Manager, go to the Performance tab, click your GPU, and look at “Dedicated GPU memory.” That’s your VRAM. Write the number down, you’ll use it to pick models.
Check your free disk space. Have at least 50 to 100GB free on an SSD to start with.

Route 1: LM Studio

Download LM Studio from its official website, lmstudio.ai, and install it like any other program. Only download from the official site.
Open it. It may suggest a starter model for your hardware. That’s a perfectly good place to begin.
Otherwise, go to the search or discover section, and search for a model family, for example “gemma 4” or “qwen3.”
LM Studio shows different versions and sizes, and usually indicates whether each one is likely to fit fully on your GPU. Pick one that fits with a couple of gigabytes to spare. A Q4_K_M version is a good default.
Click download and wait. Depending on your internet speed and the model size, this could take a few minutes or much longer.
Go to the chat section, load the model you downloaded, and start typing.
In the model settings, check that GPU offload is set to the maximum (all layers on the GPU) if the model fits. You can also adjust the context length here.

That’s it. You’re running AI locally.

Route 2: Ollama

Download Ollama from its official website, ollama.com, and install it.
Ollama runs quietly in the background. You can use its desktop chat app, or open Windows Terminal or Command Prompt.
In the terminal, type ollama run followed by a model name from the Ollama library, for example ollama run gpt-oss:20b. It’ll download the model the first time, then open a chat right in the terminal.
Type your message and press Enter. Type /bye to exit.
Use ollama list to see your downloaded models, and ollama ps to see what’s loaded and whether it’s running on the GPU or CPU. That second command is very useful for troubleshooting.

Ollama’s model library uses tags to choose sizes and versions. Check the model’s page on the Ollama website to see which tags are available and how big each one is.

Adding a nicer interface

If you want a ChatGPT-style interface on top of Ollama, Open WebUI is the popular choice. It’s usually installed with Docker or Python, which is a bit more technical. Follow the official instructions on its project page. Once it’s running, you open it in your browser, connect it to Ollama, and pick any model you’ve downloaded.

Checking it’s using your GPU

Open Task Manager while the model is generating a reply. You should see GPU usage spike and “Dedicated GPU memory” fill up. If instead your CPU usage shoots up to near 100% and generation is slow, the model is probably running on the CPU, either because it doesn’t fit in VRAM or because of a driver problem. In Ollama, ollama ps shows the percentage on GPU versus CPU.

Your first few prompts

Don’t just ask it trivia. Small local models are worst at facts, since they can’t look things up. Try tasks where they shine:

“Rewrite this email to sound more polite and professional:” followed by your draft.
“Summarise the following text in five bullet points:” followed by an article.
“Explain what this Python code does, line by line:” followed by some code.
“Translate this into simple English:” followed by technical text.
“Give me ten ideas for…” anything you’re brainstorming.

You’ll quickly get a feel for what your model does well, and when it’s time to try a bigger one.

System prompts and settings worth knowing

System prompt: instructions the model follows throughout a chat, like “You are a helpful assistant. Answer concisely in British English.” Setting a good system prompt improves results a lot.
Temperature: controls randomness. Lower (around 0.2 to 0.5) for factual and coding tasks, higher (0.7 to 1.0) for creative writing.
Context length: raise it if you work with long documents and have the memory for it.
Reasoning effort: some reasoning models, like gpt-oss, let you choose low, medium or high reasoning effort. Higher means better answers to hard problems but more time spent thinking.

Part 11: Useful things to actually do with it

Running a model is the easy part. Here’s where it actually earns its place on your PC.

Private document assistant

Load a contract, report, or set of notes, and ask questions about it. “What are the termination conditions in this agreement?” “List all the deadlines mentioned in these meeting notes.” Because nothing leaves your machine, you can do this with confidential documents. Tools like Open WebUI and LM Studio support attaching documents, and for bigger collections there’s a technique called RAG.

RAG: chatting with your own files

RAG stands for Retrieval-Augmented Generation. In simple terms, instead of pasting a whole library of documents into the model (which wouldn’t fit), the software first searches your documents for the parts relevant to your question, then gives just those parts to the model to answer from. It’s how you build an assistant that “knows” your company’s documents, your study notes, or your product catalogue.

Several tools make this approachable without coding, including built-in document features in Open WebUI and dedicated apps like AnythingLLM. The quality depends a lot on how well your documents are organised, so expect some experimentation. And remember, the model can still misread or misquote, so always check important answers against the original document.

Coding help

Local coding models have become genuinely useful. Connect one to your code editor through Ollama or LM Studio, and you get code completion, explanations, and help writing tests or fixing bugs, without uploading your code anywhere. That’s important for companies with strict rules about where their code can go. For the hardest coding problems the top cloud models are still ahead, but a good 20 to 32B local model handles a lot of everyday programming work.

Writing and editing

Drafting, rewriting, adjusting tone, shortening, expanding, fixing grammar. This is something even small local models do well. It’s great for emails, reports, social media posts and product descriptions. We’d always suggest reading and editing the result yourself though, because AI writing has a way of sounding a bit generic if you just copy and paste it.

Transcription and meeting notes

Run Whisper locally to turn recordings into text, then use your language model to summarise the transcript into notes and action points. It’s a powerful combination for students recording lectures or businesses recording meetings, and it all stays private.

Translation

Local models are handy for translating between languages, particularly between English and other major languages. Quality varies by language and model size, so test with your own content.

Learning and tutoring

Ask a model to explain a concept “like I’m 15,” quiz you on a topic, or walk you through a maths problem step by step. Reasoning models are particularly good at showing their work. Just remember that models can be confidently wrong, so double check anything important, especially facts and figures.

Automation for small businesses

With some technical help, local models can be wired into everyday workflows: categorising incoming emails, extracting details from invoices, drafting replies to common customer questions, or writing first drafts of product descriptions. Because there’s no per-use fee, these repetitive jobs can run all day for just the cost of electricity. This is one area where we think local AI will grow a lot over the next couple of years.

Image generation

Generating images locally with tools like ComfyUI is a whole hobby in itself. It’s especially VRAM hungry. 8GB will run older and smaller image models, 12 to 16GB handles most popular ones well, and newer large image and video models really want 24GB or more. It’s also where Nvidia’s software advantage is most noticeable.

Part 12: Privacy, safety and licences

Is it really private?

When the model runs fully on your machine, your prompts and documents aren’t sent anywhere by the model itself. That’s the core privacy benefit. But a few things are worth checking:

The app around the model. Some apps have optional features like cloud sync, analytics, or web search. Read the settings and turn off anything you don’t want. Reputable tools are clear about what, if anything, leaves your PC.
Web search and plugins. If you enable a feature that lets the model search the web or use online tools, then your queries go to those services. That’s not local anymore.
Who else uses the PC. Chat histories are usually saved on your drive. If the computer is shared, think about who can see them.
Network sharing. If you run Ollama or Open WebUI as a server for your home or office, make sure it’s only accessible on your local network and protected with a password, not exposed to the internet.

Only download from trusted sources

Model files and AI apps are a target for scammers, like any popular software. Download apps only from their official websites or official app stores. Download models from well-known sources like the official Ollama library, LM Studio’s built-in browser, or the official organisation pages on Hugging Face. Be wary of random “uncensored super model” downloads from forums or file sharing sites, and be especially careful with older model formats that can contain executable code. The GGUF and safetensors formats are designed to just hold data, which is safer.

Models can be wrong

Local or cloud, AI models sometimes produce confident, fluent answers that are completely wrong. This is often called “hallucination.” Smaller models do it more than larger ones. Never rely on a model for medical, legal or financial decisions without checking with a qualified person, and always verify facts, figures, names and quotes. Models are fantastic assistants, but they’re not a source of truth.

Licences matter if you’re a business

For personal use, almost any open model is fine. For business use, check the licence. Apache 2.0 and MIT licences (used by many Gemma, gpt-oss, Mistral and DeepSeek releases) are very permissive and allow commercial use. Others come with conditions. Meta’s Llama licence, for example, includes extra requirements for very large companies, and some recent models from various labs come with their own custom licences with usage policies or restrictions for large commercial operators. Its always worth reading the licence page for the exact model you plan to build on.

Part 13: Myths, mistakes and troubleshooting

Myth: “You need a supercomputer to run AI.”

Not anymore. Small but genuinely useful models run on ordinary PCs with 8GB graphics cards, and even on CPUs. You need serious hardware for the biggest models, not for useful ones.

Myth: “A faster GPU is always better for AI.”

For local language models, memory capacity usually matters more. A slower card with 16GB will often be more useful than a faster card with 8 or 12GB, because it can run bigger, smarter models entirely on the GPU.

Myth: “Local models are as good as ChatGPT.”

Some of the biggest open models are competitive with leading cloud models on many tasks. But the models most people can run at home are smaller, and they’re generally less capable than the best cloud models, particularly on hard reasoning and knowledge questions. They’re good enough for a lot of everyday work, and getting better fast.

Myth: “Running AI will damage my graphics card.”

Running models is just another heavy workload, like video rendering. A properly cooled card running within its normal limits isn’t harmed by it. Keep temperatures reasonable, keep dust under control and use a quality power supply.

Mistake: picking a model too big for your memory

The most common mistake by far. If generation is painfully slow, check whether the model is spilling into system RAM. Try a smaller model or a lower quantization (for example Q4 instead of Q6) so it fits entirely on the GPU.

Mistake: forgetting about context memory

A model that runs fine for short chats can slow down or crash when you paste a huge document, because the KV cache grows. Leave headroom, or reduce the context length.

Mistake: judging a model on trivia

Asking a small model obscure facts and declaring it useless because it got them wrong misses the point. Test it on the kind of tasks you’ll actually use it for: rewriting, summarising, coding, explaining.

Troubleshooting quick list

Very slow generation: model likely too big for VRAM or running on CPU. Check with Task Manager or ollama ps. Update the GPU driver.
Out of memory errors: choose a smaller model or quantization, reduce context length, close other GPU heavy apps like browsers with many tabs or video editors.
Model gives strange or repetitive answers: check you’re using the correct chat template (usually automatic in LM Studio and Ollama), try lowering temperature, or try a different quantization.
AMD card not used: make sure your tool’s version supports your card, update to the latest AMD driver, and try the Vulkan backend option if available.
PC shuts down under load: power supply struggling or overheating. Check PSU capacity and quality, and airflow.
Answers cut off: increase the maximum output length setting.

Part 14: Frequently asked questions

Can I run ChatGPT on my own PC?

Not the exact models behind ChatGPT, which OpenAI keeps private. But OpenAI’s open-weight gpt-oss models can be downloaded and run locally, and there are many other excellent open models from Google, Alibaba, Meta, Mistral and others.

What’s the cheapest way to get started?

Use the PC you already have. Install LM Studio or Ollama and try a small model. If you have a graphics card with 6 to 8GB of VRAM, you can run useful models today for free. Only upgrade once you know what you want to do and where your current hardware falls short.

Is 8GB of VRAM enough?

For small models, yes, and they’re useful. For larger, smarter models, no. If you’re buying a card with AI in mind, we recommend 16GB.

Should I get an RTX 5070 or RTX 5060 Ti 16GB for AI?

For local language models specifically, the RTX 5060 Ti 16GB is often the better choice because of its extra VRAM, even though the RTX 5070 is faster in many other tasks. If you also do lots of other GPU heavy work, it’s a closer call. Ask us and we’ll help you weigh it up.

Is a Mac better than a PC for local AI?

For running very large models quietly on a single machine, a Mac with lots of unified memory is excellent. For the best speed per rupee on models that fit in VRAM, for image generation, and for the widest software support, a PC with an Nvidia card is usually stronger. Both are good, it depends on your needs and budget.

What about the Ryzen AI Max mini PCs?

They’re a very interesting option if you want to run big models, especially large MoE models, without buying a huge graphics card or an expensive Mac. Generation speeds on big MoE models are good, prompt processing is slower than high-end Nvidia hardware, and software support, while much better than before, can still need some tinkering.

Do I need the internet to use it?

Only to download the app and models. After that, it runs completely offline.

How much electricity does it use?

While generating, your GPU works hard and draws power similar to other heavy tasks, from around 100 to 200 watts for mid-range cards up to 500+ watts for an RTX 5090. When idle, usage drops right down. For occasional use the cost is small. For running all day, factor it in, and remember unified memory systems like Macs and Ryzen AI Max machines are much more power efficient.

Can it access the internet or my files?

Only if you set it up to. By default, a local model only sees what you type or attach. You can add tools for web search or file access, but then you need to think about privacy again.

Can I use it for my business?

Yes, and many businesses do, especially for private documents. Check the licence of the specific model, and keep a human checking anything important it produces.

Will my model become outdated?

It won’t stop working, but newer models will keep coming out that are better. One of the nice things about local AI is that upgrading the model is free, just download a newer one. Your hardware stays useful for years as models get more efficient.

The bottom line

Running AI on your own PC has gone from a hobby for enthusiasts to something genuinely practical in 2026. Better small and medium models, permissive licences from companies like Google and OpenAI, and easy apps like LM Studio and Ollama mean that almost anyone with a reasonably modern PC can try it in half an hour.

The key thing to understand is memory. VRAM, or unified memory, decides which models you can run, and how big a model you can run decides how smart your assistant is. For most people, a graphics card with 16GB of VRAM, 32GB of system RAM and a fast 1 to 2TB NVMe SSD is the sweet spot, enough for very capable models at good speed. Go to 24 to 32GB of VRAM for the next step up, and look at 128GB unified memory systems if you want to run the really large models.

Local AI won’t replace the best cloud services for every task, and you don’t need it to. But for privacy, for heavy use without subscription fees, for working offline, and for learning how this technology really works, it’s hard to beat. And honestly, theres something quite special about watching your own computer think.

If you’re planning a PC for local AI, or want to know what your current system can handle, get in touch with us. Tell us what you want to do with it, and we’ll help you put together a build that makes sense, without paying for things you don’t need.

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