Knowledge Base · Local AI

What can a 16GB vs 24GB GPU run locally?

By Tiny · how I pick

Updated October 2026. These are the two VRAM sizes people compare most for local AI. Here's what each one runs, with real model examples, and where each runs out of room.

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A 16 GB graphics card comfortably runs models up to about 27B parameters at 4-bit. It also runs 8B to 14B models at higher quality, plus image generators like SDXL and compressed versions of Flux.

A 24 GB card handles 32B models at 4-bit with room left for the conversation. It also runs Mixture-of-Experts models and makes image generation easy. Moving from 16 to 24 GB mostly buys you the 30B-and-up class and some breathing room.

Words you'll see in this guide
  • VRAM: the memory on your graphics card. AI models load into it, so more VRAM means you can run bigger models.
  • Parameters (8B, 27B, 70B): a rough measure of a model's size. "8B" means 8 billion. Bigger models are usually smarter and need more memory.
  • Quantization (Q4, Q8): shrinking a model by storing its numbers less precisely, so it fits in less memory with a small drop in quality. Think of saving a photo as a smaller JPEG. "4-bit" (Q4) is the common setting.
  • Context: how much text the model can keep in mind at once, measured in tokens. Longer context uses more memory.
  • Mixture-of-Experts (MoE): a model design that only uses part of itself for each word it writes, so it runs faster than its total size suggests. The whole model still has to be loaded.

See the full glossary

What 16 GB runs

Example card: the RTX 4060 Ti 16GB. 16 GB is the value pick for local AI, and it covers what most people want to do:

It runs out of room at 32B. A 32B model at Q4 needs about 22 GB, so part of it spills into system RAM and everything slows down. If 32B models are what you're after, 16 GB isn't enough.

What 24 GB runs

Example cards: the RTX 3090 and RTX 4090. 24 GB is the enthusiast pick, and it handles the size that 16 GB can't:

It runs out of room at 70B. A 70B model at 4-bit needs about 40 to 48 GB, so one 24 GB card can't hold it. You'd need two cards, or to let part of the model run on the processor. Details in VRAM for Llama 3 70B.

Side by side

Model size16 GB24 GB
7B to 8BYes, at top qualityYes, at top quality
13B to 14BYes, comfortablyYes, with lots of room
About 27B (Q4)Yes, fills the cardYes, with room for higher quality
32B (Q4)No, spills into RAMYes (about 20 GB)
70BNoNo, needs two cards
SDXL, compressed FluxYesYes, faster

Numbers assume 4-bit (Q4) unless noted. The exact fit depends on the model and how long your conversations get.

Which one I'd buy: if your list stops around 27B models and SDXL-style images, get 16 GB and save the money. Go to 24 GB only if you specifically want 32B models on the card, higher-quality settings, or faster image generation, and check whether a used RTX 3090 is worth it before you pay current prices. Neither one runs a 70B model alone. That takes two cards or a Mac with a lot of memory.

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Quick answers

What can a 16GB GPU run locally?

A 16GB card runs models up to about 27B at 4-bit, runs 8B to 14B models at higher 8-bit quality, and handles SDXL and compressed Flux for images. Models of 30B and up start spilling into system RAM and slow down.

What can a 24GB GPU run locally?

A 24GB card like the RTX 3090 runs 32B-class models at 4-bit with room for context, 27B models at higher quality, most mixture-of-experts models, and image generation comfortably. A 70B model still needs two cards or part of it in system RAM.

Is 16GB or 24GB VRAM better for local AI?

24GB is meaningfully better because it fits 32B models fully and leaves room for long chats and images. 16GB is the value pick and runs what most people need, up to about 27B.

Related guides

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