No — small models run on a CPU (just slowly), and Apple Silicon Macs run models well using shared memory instead of a separate GPU; but a GPU with enough VRAM is what makes larger models run at a comfortable speed.
A common worry for people curious about running AI locally is that they need an expensive graphics card first. The honest answer is no — you can run models without a dedicated GPU — but with an important nuance about speed and size. Whether you "need" a GPU depends on how big a model you want and how fast you want it to respond.
You can run models on a CPU alone. The engine most local tools are built on, llama.cpp, supports CPU-only inference, so a machine with no dedicated graphics card can still run open models. Small models — in the 1B to 7B range, quantized to 4-bit — run at a usable pace on a modern CPU with enough system RAM, especially for short prompts and non-urgent use. The trade-off is speed: CPU generation is much slower than GPU generation, so larger models feel sluggish and long outputs take a while. For light use of small models, though, CPU-only is a perfectly real option and costs you nothing extra.
Apple Silicon Macs are a special and favorable case. They don't have a separate GPU with its own VRAM; instead the CPU and GPU share one pool of unified memory, and the GPU is genuinely capable at AI work via Apple's Metal framework. So on an M-series Mac you're effectively running on a good GPU already, and your total RAM acts as your VRAM budget — a 16, 32, or 64 GB Mac can run surprisingly large models well without any add-on hardware. If you have an Apple Silicon Mac, you don't need to buy anything to get a good local-AI experience.
Where a dedicated GPU earns its place is speed and size. To run a model fast, its weights need to fit in VRAM — the fast memory on a graphics card — so the model runs entirely on the GPU. A card with enough VRAM turns a model that crawls on CPU into one that responds at or above reading speed, and lets you run larger models than a CPU could handle comfortably. This is why enthusiasts running mid-size or large models want a GPU with generous VRAM (a 24 GB card is the popular sweet spot). If your goal is bigger models or snappy responses, a GPU is the upgrade that delivers it.
There's also a middle path worth knowing about: llama.cpp can split a model between GPU and CPU, loading as many layers as fit in your VRAM and running the rest on the CPU. So even a modest GPU helps — it speeds up the part of the model that fits, and the overflow runs on the CPU rather than the model failing to load. This graceful degradation means you don't need a GPU big enough for the whole model to benefit from having one; more VRAM simply means more of the model runs at full speed.
So the practical guidance: if you just want to try local AI or run small models for light tasks, you can start today on whatever computer you have — CPU-only for a PC without a graphics card, or natively well on an Apple Silicon Mac. If you want to run mid-size or large models, or want fast responses, a GPU with enough VRAM (or a high-RAM Mac) is what makes that comfortable. And if you don't want to buy hardware at all, renting a cloud GPU by the hour or using a pay-per-token API with your own key are the no-hardware routes — which is cheapest depends on your usage.
The cost angle is the appealing part of the no-GPU-needed truth: running a small model on a CPU or a Mac you already own costs only electricity, stays private, and works offline. You're limited by speed and size, but for many uses that's fine, and it's a genuinely $0-compute way to get started.
Spanvero helps you see exactly what your current machine can handle before you spend anything. Use /calculator/ to enter your RAM or VRAM and check whether a given model will run and how; browse the smallest, most CPU-friendly models at /best/best-small-llms/ and the laptop-friendly picks at /best/best-llm-for-a-laptop/; and read how to get started in the guide at /learn/how-to-run-your-first-local-model/.
How much RAM vs VRAM do I need for LLMs? · Can I run Llama 3 on a MacBook? · What GPU should I buy for running local LLMs? · How do I run my first local AI model? · VRAM · llama.cpp · What LLMs can I run on 8GB of VRAM? · How much does it cost to run an AI model?
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