Gemma 4 offers five distinct model variants, each optimized for different hardware and use cases. This guide helps you choose the right one.
The Gemma 4 Model Family at a Glance
| Model | Parameters | Active Params | Context | Best For |
|---|---|---|---|---|
| E2B | 2.3B | 2.3B | 128K | Mobile, IoT, edge devices |
| E4B | 4.5B | 4.5B | 128K | Laptops, tablets, edge GPUs |
| 12B | — | — | — | Unified multimodal workloads (released June 3, 2026) |
| 26B A4B | 26B | 3.8B (MoE) | 256K | Consumer GPUs, cost-efficient inference |
| 31B Dense | 30.7B | 30.7B | 256K | Maximum quality, fine-tuning, workstations |
Gemma 4 E2B: The Ultra-Compact Model
Best for: Phones, Raspberry Pi, Jetson Nano, and other resource-constrained devices.
The E2B model packs impressive capability into just 2.3 billion effective parameters. At Q4 quantization, it requires only 3.2 GB of memory — small enough to run on most modern smartphones.
Key strengths:
- Native audio input support
- Near-zero latency for simple tasks
- Fully offline operation
- 128K context window
Limitations: Lower reasoning quality compared to larger variants. Best for straightforward tasks like text summarization, basic Q&A, and quick translations.
Gemma 4 E4B: The Edge Sweet Spot
Best for: Laptops, tablets, Android/iOS apps, and edge GPUs.
E4B doubles the capability of E2B while remaining deployable on consumer hardware. At Q4 quantization, it needs about 5 GB — comfortable for most laptops.
Key strengths:
- Audio and vision capabilities
- AICore preview support on Android
- ML Kit GenAI API integration
- Strong multilingual performance
When to choose E4B over E2B: When you need better reasoning quality and have slightly more memory available. E4B is the recommended starting point for most local deployments.
Gemma 4 12B: The Unified Multimodal Variant
Best for: Workloads that mix text, images, and audio in a single pipeline.
The 12B configuration arrived on June 3, 2026, after the original April launch, and it uses a unified multimodal architecture rather than bolting separate encoders onto a text backbone. That makes it the variant to look at first when a single model has to handle more than one input type without a hand-built routing layer.
Because it shipped later than the rest of the family, published figures for parameter counts, context window, and quantized memory footprint are still settling. Check the official model card on Hugging Face for current numbers before sizing hardware around it — we will update the table above once those are confirmed.
Gemma 4 26B A4B: The Efficiency Champion
Best for: Consumer GPUs, single H100, cost-efficient production serving.
This is where the Mixture-of-Experts architecture shines. Despite having 26 billion total parameters, only 3.8 billion activate per token — delivering large-model quality at small-model inference cost.
Key strengths:
- Ranked #6 among open models on Arena AI
- 256K context window
- Ultra-fast inference with MoE efficiency
- Runs on consumer GPUs at Q4 (15.6 GB)
When to choose 26B over 31B: When inference speed and cost matter more than squeezing out the last few percentage points of quality. For most production use cases, the 26B A4B offers the best quality-to-cost ratio.
Gemma 4 31B Dense: The Flagship
Best for: Maximum quality tasks, fine-tuning, research, and workstation deployment.
The 31B Dense model is Gemma 4's most capable variant, ranking #3 among open models globally on the Arena AI text leaderboard.
Key strengths:
- Highest quality across all benchmarks
- AIME 2026: 89.2%
- LiveCodeBench v6: 80%
- Optimized for fine-tuning workflows
- 256K context window
Hardware requirements: At full BF16 precision, you need 58.3 GB (single 80 GB H100). At Q4 quantization, it drops to 17.4 GB — feasible on high-end consumer GPUs.
Decision Flowchart
- Running on a phone or tiny device? → E2B
- Running on a laptop or tablet? → E4B
- Need production serving with good cost efficiency? → 26B A4B
- Need maximum quality or plan to fine-tune? → 31B Dense
- Not sure? → Start with 26B A4B if you have 16+ GB VRAM, otherwise E4B
Memory Requirements Quick Reference
| Model | BF16 | FP8 | Q4_0 |
|---|---|---|---|
| E2B | 9.6 GB | 4.6 GB | 3.2 GB |
| E4B | 15 GB | 7.5 GB | 5 GB |
| 26B A4B | 48 GB | 25 GB | 15.6 GB |
| 31B | 58.3 GB | 30.4 GB | 17.4 GB |
These figures represent static model weights only. Actual memory usage increases with context window size, KV cache, and runtime overhead.
Conclusion
There's no single "best" Gemma 4 model — the right choice depends on your hardware constraints and use case priorities. The model family is designed so you can start small (E2B/E4B for prototyping) and scale up (26B/31B for production) while maintaining API compatibility across the entire lineup.
For most developers getting started, we recommend the 26B A4B — it offers the best balance of quality, speed, and hardware accessibility. If you're deploying to mobile or edge devices, start with E4B and drop to E2B only if memory is truly constrained.
