The right amount of RAM for local AI comes down to one question: how big a model do you want to run, and how fast do you need it? Everything else, the brand of chip, the marketing about TOPS and NPUs, is downstream of that. This guide maps memory to model size so you can buy once and not overpay for headroom you will never use.
The short version up front: 32GB runs the models most people actually want, 64GB unlocks the 70-billion-parameter class, and 128GB is for the largest quantized models and long context work. The catch is that not all memory is equal, and getting that part wrong is the most expensive mistake in a home AI build.
Table of Contents
- RAM for local AI, by tier
- RAM vs VRAM vs unified memory
- 32GB: the practical entry point
- 64GB: where 70B becomes possible
- 128GB: the biggest models and long context
- Laptop vs mini-PC vs dedicated AI box
- A worked example: size it yourself
- What this costs in Canada
- RAM for Local AI FAQ
- The Short Version
RAM for local AI, by tier
Modern local models are run quantized, usually at 4-bit (the Q4_K_M format), which cuts memory use to roughly a quarter of the full-precision size while losing only 1 to 3% of quality on standard benchmarks. That is the setting almost everyone should use, and it is what the numbers below assume. The figures include the model weights plus the overhead for context and runtime, drawn from published Ollama and llama.cpp footprints.
Read that chart as a shopping guide. An 8-billion-parameter model needs about 6GB, a 14B model about 10GB, a 32B model about 20GB, and a 70B model about 44GB. Add the memory your operating system and other apps use, round up, and you land on the tier you actually need.
RAM vs VRAM vs unified memory
This is the part the spec sheets bury, and it decides whether your money buys speed or frustration. There are three kinds of memory in play, and only the right kind makes a model fast.
VRAM is the memory on your graphics card. Models run fast when they fit entirely in VRAM, because the GPU never has to wait on slower system memory. System RAM is what your sticks of DDR5 provide. A model can run from system RAM on the CPU, but it is many times slower, often the difference between a usable assistant and a slideshow. Unified memory is one shared pool the CPU and GPU both address at full speed, used by Apple Silicon, the NVIDIA RTX Spark platform, and AMD’s Ryzen AI Halo.
Here is why it matters for your RAM for local AI budget. A desktop with 128GB of DDR5 but only an 8GB graphics card cannot run a 70B model quickly, because only 8GB of that model fits in fast VRAM and the rest crawls on the CPU. A machine with 128GB of unified memory runs the same model at usable speed because the GPU can reach all of it. When you compare two machines with the same memory number, check which kind it is first. The rest of this guide talks about the memory the model can actually use, not the number on the box.
32GB: the practical entry point
For most people getting into local AI, 32GB is the sweet spot. Paired with a 16GB graphics card, it comfortably runs 8B and 14B models entirely in VRAM, which covers coding assistants, chat, summarization, and general-purpose work. Those are the models that do 90% of what people actually want, and they run fast on hardware you might already own.
You can load a 32B model at this tier too, but it will spill past a 16GB card and lean on system RAM, so expect a real speed drop. Treat 32B as occasional, not your daily driver, unless you move up a tier. If you are building fresh and local AI is a core reason, spend the budget on a bigger GPU before a bigger pile of system RAM.
64GB: where 70B becomes possible
64GB is the tier where the 70B class comes into reach, needing about 44GB with context headroom. This is the jump that matters if you want noticeably stronger reasoning, better code, and longer, more coherent output than the smaller models manage. On a unified-memory machine, 64GB runs a 70B model at usable speed. On a traditional desktop, you still need enough VRAM to hold the model, or you are back to the slow CPU path.
This is also the tier where mini-PCs get interesting. A 64GB unified box like the Lenovo Yoga mini-PC can run models a desktop with a mid-range GPU simply cannot fit, in a fraction of the space and power. If a 70B model is your target and you do not need a gaming rig, that route is worth a serious look.
128GB: the biggest models and long context
128GB is for two things: the largest quantized models, including the 120B-class mixture-of-experts models that need roughly 65 to 80GB, and long context work where you feed the model huge documents and the context cache balloons. It is genuine headroom, not a number for bragging rights, and most people do not need it.
Where 128GB earns its price is unified-memory machines built for exactly this, like the current RTX Spark systems and the Beelink GTR9 Pro with its 128GB pool. If you are running the frontier of what is possible at home, or you handle sensitive data and want a capable model fully offline as covered in our home AI agent guide, this is the tier. If you are running an 8B coding assistant, it is money left on the table.
Laptop vs mini-PC vs dedicated AI box
The same memory tier behaves differently across form factors. A laptop gives you mobility, but its memory is usually soldered, so you buy your tier at checkout and live with it for the life of the machine. Spec up, because you cannot add more later. A mini-PC with unified memory is often the best value for local AI specifically: it delivers a large fast memory pool in a small, quiet, low-power box, which is exactly what an always-on model server wants. A traditional desktop is the most flexible and upgradeable, and the right choice if you also game or need a big discrete GPU, but you pay for that GPU to get enough VRAM.
The trap is buying a desktop with a huge system RAM number and a small GPU, expecting it to run big models. It will run them, just slowly. Match the machine to the memory type, not the memory number.
A worked example: size it yourself
Say you want to run a 32B model as your daily assistant with a decent amount of context. Start with the weights: about 20GB at 4-bit. Add context overhead, which grows with how much text you feed it, so budget a few extra GB for comfortable use. That puts the model itself around 24GB of memory it can actually reach at speed.
Now add your operating system and normal apps, call it 8 to 12GB. You land near 36GB of working memory, which is why a 32B daily driver really wants a machine with a 32GB-plus fast memory pool, not a 16GB GPU. The method is always the same: model weights at 4-bit, plus context headroom, plus your system’s own use, then round up to the next tier. Assumptions here are 4-bit quantization and moderate context; heavier context or higher precision pushes you up.
What this costs in Canada
Memory is unusually expensive right now, which makes sizing correctly worth real money. As of mid-July 2026, a 32GB DDR5-6000 kit runs about CA$620.99 (roughly US$443) and a 64GB kit about CA$1,299.00 (roughly US$927), the verified in-stock prices from our RAM price tracker. Those numbers are climbing, not falling, for reasons covered in the RAM shortage 2027 outlook.
For a traditional build, the bigger cost is the graphics card that supplies the VRAM, not the system RAM. For a unified-memory machine, the memory is baked into the price of the box, which is why those mini-PCs look expensive until you price the equivalent GPU. Before you commit either way, it is worth reading whether to buy now or wait in our RAM prices guide, because the memory market is the single biggest variable in a 2026 AI build.
RAM for Local AI FAQ
Is 16GB enough for local AI?
It is enough to start. 16GB of VRAM runs 8B and 14B models well, which covers most everyday assistant and coding tasks. You will just hit a ceiling around the 14B class and cannot reach the 32B or 70B models that need more.
Does system RAM or VRAM matter more for local AI?
For speed, the memory the GPU can reach matters most: VRAM on a traditional PC, or the unified pool on an Apple, RTX Spark, or Ryzen AI Halo machine. System RAM only helps if you accept the slow CPU path, so prioritize fast memory the model fits into entirely.
How much RAM for local AI do I need to run a 70B model?
About 44GB at 4-bit with context headroom, so a 64GB tier in practice. On a unified-memory machine that runs at usable speed; on a traditional desktop you need a graphics card with enough VRAM to hold it, which is the expensive part.
Can I just add more system RAM instead of buying a bigger GPU?
Only if you accept slow, CPU-bound speeds. Adding DDR5 lets a bigger model load, but it will not run fast unless the GPU can address that memory. On a traditional PC, VRAM is the real constraint; on a unified-memory machine, adding to the shared pool does help because the GPU reaches all of it.
The Short Version
Buy 32GB if you want 8B to 14B models for coding and chat, 64GB if you want the 70B class, and 128GB only if you are running the largest models or long-context work. Then check the memory type: fast VRAM or unified memory is what makes a model quick, and a big system RAM number next to a small GPU is the classic overspend that still runs slowly.
Size the model first, match the memory type second, and buy the tier that fits, not the biggest one on the shelf. That is the whole of getting RAM for local AI right.