On April, 2, 2026 Google DeepMind released Gemma 4 – a family of four models which expands the open stack with multiple sizes from 2B to 31B parameters. Gemma has always been a good model. But when it was first launched, there was no use case like OpenClaw, or other open-source agents, the way there is now. So we decided to cover it used with OpenClaw to make it more practical. | One thing is super interesting about Gemma 4. It is pushing a different axis of progress: intelligence per parameter and per unit of compute. Previously, the focus for many open models was to achieve maximum capability at a given scale. Now it has shifted to the hardware possibilities. The same core ideas, such as sparse activation, efficient attention, and multimodal processing, are expressed differently depending on whether the target is a phone, a laptop GPU, or a high-end accelerator. This means that DeepMind makes high-level intelligence accessible across the entire hardware spectrum, betting on adoption among ordinary users and local developers. | That’s why the Gemma 4 release rapidly raised a wave of adoption in OpenClaw, becoming the new default local candidate to try first. | Today we explain how Gemma 4 models get frontier-level performance out of much smaller effective compute budgets through architectural choices, and what makes them so appealing for users to switch to (or at least try) in OpenClaw. Here is your guide to one of the most capable model your hardware can sustain. | | In today’s episode: | What is Gemma 4 notable for? How Gemma 4 Works: Architecture Is Everything Attention Mix: Local + Global Five Special Optimizations for Global Attention The vision pipeline E4B and E2B Model Specifics
Why many OpenClaw users are now switching to it? Why Some Users are Pushing Back Conclusion Sources and further reading
| What is Gemma 4 notable for? | Gemma 4 is presented as a family of open models built from the same research and technology stack as Gemini 3. But the more interesting point is what Google is optimizing for: intelligence per parameter and per unit of compute. In practice, that means pushing more reasoning, coding, and multimodal capability into models that can run on smaller hardware budgets. | It is a deployment-aware family structured around distinct hardware targets and inference budgets and divided into two groups: | E2B (effective 2B) and E4B (effective 4B) These models are designed for edge devices, providing near-zero latency, low memory usage and battery efficiency. They are multimodal (vision + audio) and run fully offline on devices like phones, Raspberry Pi, Jetson board or small embedded systems. 26B A4B (Mixture-of-Experts) and 31B (dense)
These two variants are designed for local frontier-level reasoning performance. In BF16 precision format, both fit within a single 80GB H100’s memory budget (about 48 GB for 26B A4B and 58.3 GB for 31B), and lower-bit quantized versions can run on smaller local GPUs, turning a workstation into your local AI server. They can process images but don’t process audio data.
|  | Image Credit: A Visual Guide to Gemma 4 by Maarten Grootendorst |
| Gemma 4, in line with today’s demands, is structured around agentic workflows: models support native function calling, structured JSON outputs, system-level instruction handling. This turns the new Gemma 4 into a general-purpose reasoning engine with multimodal capabilities by default and support for more than 140 languages. | Gemma 4 is rather competitive: | |  | Image Credit: Gemma 4 model page |
| Even the smaller models remain competitive for their size: E4B reaches ~52% on coding tasks and E2B still handles basic reasoning and multimodal tasks. | This validates the core idea of this release: you can get near-frontier performance without scaling model size proportionally, and this shows up clearly in how the models are designed. | How Gemma 4 Works: Architecture Is Everything | If we go deeper into the Gemma 4’s architecture, we’ll see the following structure. | Two of the larger family members have different but concrete architectures: | The 31B model is a dense Transformer, meaning that all parameters are active for every token. Compared to its predecessor, it has fewer layers, but larger hidden dimensions to improve parallelism and throughput without changing the overall structure much. The 26B A4B model introduces sparsity through a Mixture-of-Experts (MoE) architecture with 26 billion total parameters and only 3.8B parameters active during inference (this is what A4B stands for: ~4B active parameters). In practice, this means you get something that behaves closer to a large 26B model in terms of capability, while paying a compute cost closer to a much smaller one. As for the workflow, in this Gemma variant, a routing mechanism activates only 8 from the total 128 experts along with a shared expert that is always used.
| At the other end, there are two small, dense models with 2B and 4B effective parameters: E2B and E4B. They approach the same problem – capacity vs. active compute – from a different angle. Small Gemmas use per-layer embeddings, which compress how representations are stored across the network, so the effective footprint is smaller than what a naive scaling would suggest. That’s why you can use them on edge devices. (We will discuss their features more precisely a little bit later.) |  | Image Credit: A Visual Guide to Gemma 4 by Maarten Grootendorst |
| Even though all Gemma 4 variants target very different hardware setups, they share the same underlying structure and components that define a common architectural backbone of efficiency → | Attention Mix: Local + Global | Don’t settle for shallow articles. Learn the basics and go deeper with us. Truly understanding things is deeply satisfying. | | Join Premium members from top companies like Microsoft, NVIDIA, Google, Hugging Face, OpenAI, a16z, plus AI labs such as Ai2, MIT, Berkeley, .gov, and thousands of others to really understand what’s going on in AI. |
|
|
|
