2 Apr 2026
gemma-4-E4B-it
More effective capacity, with tables still to place
8B reported total · text + image + video + audio
Gemma / Dense
gemma-4-12B-it
One model path for text, vision and audio
New to model sizes or GPU memory? Start with weights, parameters and quantization →
Gemma 4 12B is especially worth studying when your input is genuinely multimodal. Its encoder-free design changes the usual picture of a language model with a separate vision or audio encoder attached.
11.95Breported total parameters
Denseactive parameters per token
262,144configured token positions
Publisher-reported, rounded model count; not an exact tensor census. Multimodal components and packaging can change the artifact size.
Evaluate it for
Applications combining text, image, video and audio inputs.
Choose another path when
A runtime or memory model that assumes every Gemma 4 variant has the same separate encoders.
Release context
Why this release matters
The June addition introduced a unified input path within the Gemma 4 family. It is an architectural branch, not simply a halfway size between E4B and 26B-A4B.
Deployment starting points
Which hardware, how many, at what precision?
Start with an exact artifact and an engine that supports it. A configuration below is evidence of a documented path; its device count does not promise a particular throughput or concurrency.
No certified hardware configuration in this edition
A runtime or memory model that assumes every Gemma 4 variant has the same separate encoders.
The pinned model card establishes the architecture. It does not, by itself, prove that a particular GPU count runs this checkpoint at your target context. The capacity screen below helps rule out undersized allocations; a successful load and workload test are still required.
Capacity screen: would the weights fit?
This estimates inference memory for a hypothetical uniform precision. It is useful for rejecting an allocation that is too small. It does not establish a working deployment or estimate training memory.
| Representation | Approximate weight floor | What remains to budget |
|---|---|---|
| 16-bit uniform | 23.9 GB | Cache, modality state, workspace, quantization metadata and uneven sharding |
| 8-bit uniform | 11.95 GB | Cache, modality state, workspace, quantization metadata and uneven sharding |
| 4-bit uniform | 5.975 GB | Cache, modality state, workspace, quantization metadata and uneven sharding |
Enter the total across the whole model replica, not per GPU: KV or recurrent cache for all concurrent requests, image/audio state and execution workspace. There is no universal default. We additionally leave 15% of each GPU unused.
23.9 GB for hypothetical weights
Enter a request-and-buffer budget to calculate the aggregate capacity requirement.
A count of one does not prove the engine supports this GPU. A count above one does not prove the model shards evenly or communicates efficiently. Publisher-reported, rounded model count; not an exact tensor census. Multimodal components and packaging can change the artifact size.
Architecture in practice
What the serving engine has to do
The input path is a model choice, too.
Top: a common encoder-based pattern.
Bottom: the unified-design distinction; preprocessing still exists.
Mechanism schematic based on the pinned model card and configuration. It explains a design principle; it is not a full implementation graph.
Inspect the full checkpoint specifications
| Exact checkpoint | google/gemma-4-12B-it |
|---|---|
| Text model type | gemma4_unified_text |
| Layers | 48 |
| Hidden width | 3,840 |
| Attention / KV heads | 16 / 8 |
| Head dimension | 256 |
| Routed / selected experts | Not verified / Not verified |
| Configured positions | 262,144 |
| Documented extension | Not recorded for this checkpoint |
| Inputs → output | text, image, video, audio → text |
| License metadata | apache-2.0 |
40 sliding attention layers; 8 full attention layers. These counts describe the configured pattern.
Open weights do not imply unrestricted use. Read the applicable license terms linked from the pinned card.
A useful comparison
What else belongs on the shortlist?
E4B is the smaller multimodal baseline; 26B-A4B trades a larger store for sparse compute and has different input support.
2 Apr 2026
gemma-4-26B-A4B-it
Sparse compute in a workstation-sized family
25.2B reported total · text + image + video
These are editorial comparison candidates. We have not run a matched quality or serving benchmark, so this is not a ranking.
Prepare to explain it
The interview lesson
Draw an encoder-based multimodal pipeline and compare it with unified token processing. Include preprocessing and first useful output in latency.
Attention layouts · Quantization · Courses and worked examples
For a deployment evaluation, record exact weights, precision, engine, device count, interconnect, prompt/output lengths and concurrency. Report task success, errors, TTFT, TPOT and useful throughput together.
Sources and citation
Our assessment is editorial judgment based on the linked architecture and deployment evidence. Model facts are publisher-reported or attributed to runtime maintainers; no independent GPU benchmark was run.
AI Infra Interviews, “gemma-4-12B-it: release, architecture and deployment”, checked 2026-09-13. Preserve this date and the exact checkpoint when citing.