Gemma 4 VRAM requirements: what do E4B, 26B, and 31B really need?
If you are searching for Gemma 4 VRAM requirements, you do not want vague advice. You want planning numbers you can use. For practical local, quantized setups, a good rule of thumb is: E4B usually lands in the 4GB to 6GB class, 26B A4B is more realistic in the 12GB to 16GB class, and 31B is far more comfortable in the 20GB to 24GB class.
Part of the hub: if you want the bigger picture behind hardware sizing, start from the full Gemma 4 guide and use this page as the detailed VRAM reference.
Quick answer
Fast planning rule: start with E4B if you are unsure, consider 26B A4B once you are in the 12GB to 16GB range, and treat 31B as a 24GB-class decision.
These are not theoretical BF16 maximums. They are rough planning ranges for the quantized local setups people actually try on consumer hardware. Google's launch materials make the split clear: the larger unquantized Gemma 4 models target high-end accelerators, while local consumer deployments lean on quantized variants.
Gemma 4 VRAM planning table
| Model | Rough local planning range | Safer hardware target | Best fit |
|---|---|---|---|
| E2B | About 2GB to 4GB | Very small local or edge-friendly setups | Proof-of-life tests and tiny deployments |
| E4B | About 4GB to 6GB | 6GB GPU or 16GB Mac as a starting point | The best first local run for most people |
| 26B A4B | About 12GB to 16GB | 16GB-class hardware feels much safer | People who want better reasoning without jumping straight to the heaviest tier |
| 31B | About 20GB to 24GB | 24GB+ is the comfortable zone | Quality-first local use, heavier coding, and longer contexts |
What if you only know your GPU tier?
| Your machine | Reasonable starting point | What to avoid |
|---|---|---|
| 6GB GPU | E4B | Do not treat 26B or 31B as default choices |
| 8GB to 12GB GPU | E4B comfortably, larger models only with care | Do not ignore context and runtime overhead |
| 12GB to 16GB GPU | 26B A4B becomes realistic | Do not confuse “boots” with “pleasant daily use” |
| 24GB+ GPU | 31B starts making sense | Do not assume VRAM is the only limit if your prompts are huge |
Apple Silicon is a memory question, not a marketing-tier question
On Mac, the number that matters most is unified memory. A simple rule of thumb:
- 16GB: start smaller and validate the workflow first.
- 24GB to 32GB: better day-to-day room for local use.
- 48GB+: much more realistic if you want to evaluate 31B-class local use seriously.
Why these numbers are only planning ranges
Because real usage includes more than model weights. Context length, KV cache, runtime overhead, and other apps all consume memory. If you are right at the boundary, the chart tells you whether you can begin testing, not whether the setup will feel great for every task.