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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteGemma 4 now has five model sizes, not four. Google added 12B Unified after the initial March 31, 2026 release. For mobile or edge deployments, start with E2B or E4B; for laptop- or server-class hardware, compare 12B Unified and 26B A4B; and consider 31B when a large-server deployment can accommodate it. The right choice depends on where you will run it, which inputs you need, context length, available memory, and how well it performs on your actual coding tasks.
What changed from the original four-model lineup?
The initial Gemma 4 release on March 31, 2026 included E2B, E4B, 26B A4B, and 31B. Google’s release history lists 12B Unified as added on June 3, 2026, bringing the current family to five choices. See Google’s release history and model card.
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Which Gemma 4 model should you use?
Use your deployment target and required input types to narrow the list first. Google’s platform guidance is a starting point, not a guarantee of performance on a specific device.
| Model | Architecture and parameter details | Context | Documented inputs | Starting point for |
|---|---|---|---|---|
| E2B | Small effective-parameter model with per-layer embeddings; 2.3B effective parameters, 5.1B including embeddings | 128K | Text, image, audio | Mobile or edge deployment where size is the main constraint |
| E4B | Small effective-parameter model with per-layer embeddings; 4.5B effective parameters, 8B including embeddings | 128K | Text, image, audio | Mobile or laptop use where the additional capacity over E2B is useful |
| 12B Unified | Encoder-free dense model; raw image patches and audio waveforms are projected into the LLM’s embedding space | 256K | Text, image, audio | Laptop, desktop, or small server when audio and longer context matter |
| 26B A4B | Mixture of Experts; 25.2B total parameters and 3.8B active parameters | 256K | Text, image | General-purpose larger-model evaluation on a laptop, desktop, or small server that can load it |
| 31B | Dense model; 30.7B parameters | 256K | Text, image | Large-server or server-cluster deployments |
Google’s developer guide gives those platform categories as intended deployment guidance. All five models support image input; the model card lists audio input for E2B, E4B, and 12B Unified, not for 26B A4B or 31B.
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Choose E2B or E4B for edge-oriented deployment
E2B is the smaller starting point when mobile or edge constraints dominate. E4B is the alternative to evaluate when the target is a phone or laptop and you need more capacity than E2B offers. Both have a 128K context window and documented text, image, and audio inputs.
Consider 12B Unified when audio and context matter
12B Unified combines documented audio input with a 256K context window. Google’s deployment guidance places it on laptops, desktops, and small servers; confirm actual memory and latency on the hardware and runtime you intend to use.
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Evaluate 26B A4B without mistaking active parameters for model size
Google recommends the latest 26B A4B as a general place to start for many tasks, citing its lower resource requirements. That is Google’s general guidance, not a universal result for every application. Although 3.8B parameters are active per token, the model has 25.2B total parameters and all experts must be loaded. The active count therefore does not tell you the full weight-memory requirement.
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Reserve 31B for deployments that can accommodate a larger dense model
31B is the largest dense option in the lineup, with 30.7B parameters. Google maps it to large servers or server clusters. Evaluate it when your deployment budget permits and your coding task may benefit from the larger option; published documentation alone cannot establish that it will outperform another size on your codebase.
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How much memory do you need to run Gemma 4 locally?
Google’s published estimates below include 20% overhead for loading additional items, but exclude supporting software and context-window memory. They are planning figures, not guaranteed minimum hardware requirements. Larger prompts and generated responses increase KV-cache memory, and results vary by inference tool and environment. The mobile figures are separately labeled by Google; do not assume they apply to every device or runtime. Source: Google’s official overview (2026).
| Model | BF16 | SFP8 | Q4_0 | Mobile estimate | Mobile text-only estimate |
|---|---|---|---|---|---|
| E2B | 11.4 GB | 5.7 GB | 2.9 GB | 1.1 GB | 0.84 GB |
| E4B | 17.9 GB | 8.9 GB | 4.5 GB | 2.5 GB | 2.2 GB |
| 12B | 26.7 GB | 13.4 GB | 6.7 GB | not stated (Google AI for Developers, 2026) | not stated (Google AI for Developers, 2026) |
| 26B A4B | 57.7 GB | 28.8 GB | 14.4 GB | not stated (Google AI for Developers, 2026) | not stated (Google AI for Developers, 2026) |
| 31B | 69.9 GB | 34.9 GB | 17.5 GB | not stated (Google AI for Developers, 2026) | not stated (Google AI for Developers, 2026) |
These estimates do not include extra memory for the context window or supporting software. In practice, account for model weights, runtime overhead, KV cache at your intended prompt and generation lengths, and any other workloads sharing the device. Quantization reduces the published weight estimate, but does not remove those additional requirements. A listed figure—especially a Q4_0 estimate—is not by itself proof that a given GPU or system will run a workload acceptably.
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How should you compare models for a coding project?
- Choose the deployment setting. For a phone or edge device, begin with E2B and E4B. For a laptop, desktop, or small server, include 12B Unified and 26B A4B if memory permits. For a large server or server cluster, include 31B in the evaluation.
- Match the required inputs. If your application needs audio input, limit the initial comparison to E2B, E4B, and 12B Unified. All five list image input.
- Check context requirements. E2B and E4B have 128K context; the other three have 256K. A larger context limit does not guarantee equal speed, quality, or memory use at every prompt length.
- Estimate memory for your intended precision and workload. Use Google’s figures as a starting point, then budget for runtime, KV cache, and the actual context length. For 26B A4B, plan around the full model weights rather than only the active-parameter count.
- Run the same coding evaluation on each candidate. Use representative tasks from your project and keep prompts, inference settings, latency requirements, and failure criteria consistent. Track whether outputs compile or pass tests, follow project conventions, and handle the changes you need. Google’s guide recommends setting up a development environment and prompting and evaluating responses; it does not establish which model will fit your codebase best.
Google’s model card also reports instruction-tuned MMLU Pro results: E2B 60.0%, E4B 69.4%, 12B 77.2%, 26B A4B 82.6%, and 31B 85.2% (Google DeepMind, 2026). These are published results for that benchmark and tuning condition, not an independent comparison or a forecast of coding performance on your project. See the model card.
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Where can you get Gemma 4, and what does its license say?
Google labels Gemma 4 open-weight and lists Apache 2.0. Its documentation says the models can be downloaded from Kaggle and Hugging Face. Review Google’s model card and overview for current access and license details; this article does not interpret the license terms.
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