How much memory does Gemma 4 12B need?

About 9.2 GB at Q4_K_M with an 8K context. It runs on a Mac with 16 GB of unified memory or more.

Try other settings in the calculator

Memory by quantization and context

Total memory: weights, KV cache in F16 and about 1 GB of buffers. Under each total, the smallest Mac that can run it with the default GPU memory limit.

Quantization8K context32K context128K context
Q3_K_M7.7 GB16 GB Mac9.3 GB16 GB Mac16 GB24 GB Mac
Q4_K_M9.2 GB16 GB Mac11 GB16 GB Mac17 GB24 GB Mac
Q5_K_M10 GB16 GB Mac12 GB24 GB Mac18 GB32 GB Mac
Q6_K12 GB16 GB Mac13 GB24 GB Mac20 GB32 GB Mac
Q8_015 GB24 GB Mac16 GB24 GB Mac23 GB32 GB Mac
F1626 GB48 GB Mac27 GB48 GB Mac34 GB48 GB Mac

About the model

Parameters
11.96B
Type
Dense
Attention
sliding window plus full attention
KV cache, F16
0.9 GB at 8K, 8.9 GB at 128K
On Hugging Face
google/gemma-4-12B-it

Some layers only look at the last 1,024 tokens, so their part of the cache stops growing. The full-attention layers still grow with context.

Questions

How much memory does Gemma 4 12B need?

About 9.2 GB at Q4_K_M with an 8K context: 7.3 GB of weights, 0.9 GB of KV cache and about 1 GB of buffers. At 32K context it needs about 11 GB.

Can Gemma 4 12B run on a Mac?

Yes. At Q4_K_M with an 8K context it fits on a Mac with 16 GB of unified memory or more, using the default GPU memory limit of about three quarters.

How big is the Gemma 4 12B KV cache?

About 0.9 GB at 8K tokens and 8.9 GB at 128K tokens with an F16 cache. Its attention design: sliding window plus full attention.

Other models

Data read from Hugging Face on 2026-11-09. These are estimates; benchmarks on this site report measured memory. How the estimate works. Compare all models in the model fit chart.

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