Model · Gemma 4 · May 2026

Gemma 4 12B

A 12 billion-parameterHow many numbers the model learned during training — the usual rough measure of its size. dense model from Google DeepMind.

Size
12Bdense
Memory to run
—smallest, 8K ctx
Context
256Ktokens
License
License unknown
Updated
23 May 2026

Where it runs

Best-fitting version on each reference system, at an 8K contextHow much text the model can consider at once, counted in tokens — roughly ¾ of a word each..

No downloadable versions recorded yet.

Measured throughput

No performance measurements recorded yet.

Versions & downloads

Each version is a separate set of weights. Expand one for its downloads: quantizationStoring each of the model’s numbers with fewer bits, so the file is smaller and needs less memory. trades a little quality for a much smaller file, and the memory column adds the working memoryExtra space the model needs while it answers. It grows with the length of the conversation, on top of the file itself. a conversation needs on top.

Benchmarks

Each benchmarkA fixed set of questions every model is given, so their scores can be compared on the same task. score is labelled with who produced it. Developer-reported scores use each lab’s own prompts and settings; a benchmark that runs every model itself uses one setup for all. Bars are relative to the best open result.

No benchmark results recorded for this model.

Community

Benchmark runs and reviews from people running it on their own hardware.

Benchmark runs

Contributions are not open yet, so there is nothing here from members.

Reviews

Contributions are not open yet, so there is nothing here from members.

Details & sources

Architecture

Parameters
11.96B
Active / token
All (dense)
Architecture
Dense
Layers
48
Attention heads
16
KV heads
8
Head dim
256
KV cache @ 8K (fp16)
3.00 GiB
Max context
262,144 tokens

Lineage

How this model’s versions relate to each other and to other models.

Sources

No source records linked.

Timeline

  1. Gemma 4 released