Will It Run? Local AI on Your PC
Pick your RAM and GPU VRAM — or your Mac's unified memory — and see instantly which local AI models run smoothly, which are usable but slow, and which won't fit. No cloud, no subscription.
Will It Run? — Local AI on Your PC
A real AI model can run entirely on your own machine — no cloud, no subscription. The only question is which size fits. Pick your hardware below and see which models run smoothly, which are usable but slow, and which won't fit.
- Smooth — the model fits entirely in your GPU VRAM (or your Mac's unified memory) with headroom, so it runs on the GPU — fast.
- Usable — too big for VRAM but it fits in system RAM, so it runs on the CPU or partially offloaded — it works, just slower.
- Won't fit — not enough memory to load it.
Sizes assume Q4_K_M quantization (the common "good enough, 4× smaller" format), at a modest context length. Real numbers vary with context size and the exact build — treat this as a planning estimate, not a guarantee. Curious what these numbers actually mean? The Hardware Behind the Tokens walks through VRAM, bandwidth, and the KV cache interactively.
Plain-English glossary
- VRAM
- Your graphics card's own fast memory. A model that fits here runs on the GPU — the quick path.
- Unified memory
- On Apple Silicon, the CPU and GPU share one memory pool, so "RAM" and "VRAM" are the same thing — which is why Macs punch above their weight for big models.
- Parameters (B)
- A model's size in billions of weights. More parameters usually means smarter, but needs more memory.
- Quantization (Q4)
- Compressing those weights to ~4 bits each instead of 16 — roughly 4× smaller, with a small quality trade-off. It's what makes local AI fit on normal hardware.
- Tokens / sec
- How fast the model writes. On the GPU it feels instant; on the CPU it can be a slow crawl.
- Context window
- How much text the model can "hold in mind" at once. Bigger context eats more memory on top of the model itself.