DEV Community: Nirbhay Gautam

Gemma 4 Has Four Models. Here's Which One You Actually Need

Nirbhay Gautam — Fri, 08 May 2026 10:58:32 +0000

Google called it one launch. It's not.

Gemma 4 is four completely different models with different architectures, different hardware requirements, and different use cases — packaged under one name that makes it sound like a single thing. If you read the announcement and walked away confused about what to actually download, that's not on you. That's the naming.

I've been building with local AI for a while — I recently built a RAG system using Llama 3.2 running locally via Ollama, and the hardware reality of running LLMs on a regular laptop is something I've dealt with firsthand. So let me break this down practically, not theoretically.

First: What "E" and "A" Actually Mean

The naming convention is doing a lot of work here and Google doesn't explain it upfront.

E2B and E4B — the "E" stands for effective parameters. These are not 2B and 4B parameter models in the traditional sense. They use Per-Layer Embeddings (PLE) to pack more capability into fewer parameters. Think of it as parameter efficiency — more intelligence per byte than the raw number suggests.

26B A4B — the "A" stands for active parameters. This is a Mixture-of-Experts (MoE) model with 26B total parameters, but during inference, only a 4B subset activates per token — making it run almost as fast as a 4B-parameter model. You get the quality of a large model at the speed of a small one.

31B Dense — no tricks. Every token touches all 31 billion parameters. Slower, heavier, but the most predictable behavior.

The Four Models, Plainly

E2B — For the Edge

Google's own tests show Gemma 4 E2B running on a Raspberry Pi 5 at around 7.6 tokens per second — slow but functional for edge agent workflows.

If you're building something that needs to run on a phone, a microcontroller, or offline hardware with no GPU — this is your only option in the family. It supports audio natively, which the larger models don't. Context window tops out at 128K.

Who it's for: IoT projects, on-device apps, offline deployments, Raspberry Pi builds.
Who it's NOT for: Anyone who wants quality answers on a regular laptop.

E4B — The Practical Daily Driver

E4B runs comfortably on any modern laptop — Mac, Windows, Linux — and delivers surprisingly good quality for its size.

This is the model most developers should start with. It handles image input, audio, and text. It's fast enough for interactive use and doesn't require a dedicated GPU. Context window is 128K.

Who it's for: Developers on regular laptops, multimodal projects that need audio, quick prototyping.
Who it's NOT for: Tasks requiring deep reasoning or complex long-document analysis.

26B A4B — The Hidden Best Value

This one is the most interesting in the lineup and the least talked about.

The 26B A4B achieves roughly 97% of the dense 31B model's quality while activating only 3.8B parameters per token — about 8x less compute per inference step. On the LMArena leaderboard it scores 1441 Elo versus 1452 for the 31B — a gap that's invisible in most real-world tasks.

If you have a machine with 16GB+ RAM and a decent GPU, or Apple Silicon, this is arguably the best model in the whole lineup. You get near-31B quality at a fraction of the compute cost. Context window extends to 256K here.

Who it's for: Developers with a decent machine who want maximum quality-per-compute, agentic workflows, long-document tasks.
Who it's NOT for: Low-spec machines, anyone without at least 16GB RAM.

31B Dense — Maximum Quality, Maximum Cost

The 31B model currently ranks as the #3 open model in the world on the Arena AI text leaderboard. Every token touches all 31 billion parameters. No shortcuts.

It's slower than the 26B A4B for inference, but it's the better candidate if you want to fine-tune — dense architecture means cleaner gradient flow during training. Context window is 256K and it actually works: the 31B went from 13.5% to 66.4% on multi-needle retrieval tests, meaning the model can actually find and reason over information buried deep in a long document.

Who it's for: Server deployments, fine-tuning projects, maximum quality use cases, cloud inference.
Who it's NOT for: Anyone running locally without a workstation-grade GPU.

The Hardware Reality Nobody Talks About

Here's my honest take as someone who's actually tried running LLMs locally on consumer hardware:

A few weeks ago I built a job market Q&A system using Llama 3.2 running locally via Ollama. The setup worked — but every response took 10-15 seconds on my CPU, and I spent more time watching a blinking cursor than actually using the thing.
I stuck with local anyway, not because it was convenient, but because the alternative was sending job description data and user queries to an external API I don't control. For a portfolio project that's fine. For anything with real user data, that tradeoff stops being theoretical.
And that's the honest hardware reality nobody talks about: the gap between "this model CAN run on your laptop" and "this model runs well on your laptop" is real and wide.
E2B and E4B are the only Gemma 4 models most people can realistically run locally without a dedicated GPU. The 26B A4B and 31B are cloud or workstation territory for most developers.

That experience is what made Gemma 4's range genuinely interesting to me — not the benchmarks, but the fact that someone with a decent laptop can now run a near-31B quality model locally without a GPU, or fall back to the 31B on OpenRouter's free tier without sacrificing open-weight guarantees. The hardware ceiling is still real.

Quick Decision Guide

Your situation	Use this
Raspberry Pi / phone / IoT	E2B
Regular laptop, need audio	E4B
16GB RAM + decent GPU	26B A4B
Server / fine-tuning / max quality	31B Dense
No GPU, want best quality	31B via OpenRouter (free)

Final Thought

Gemma 4 is genuinely impressive — not because any single model is revolutionary, but because the family covers the full deployment spectrum from a Raspberry Pi to a workstation under one open license. That's rare.

But "Gemma 4" is not one thing. Pick the right model for your hardware, your use case, and your deployment target. The name is marketing. The specs are what matter.

I'm a Final-Year CS Student — And I'm Done Letting AI Tools Own My Data

Nirbhay Gautam — Fri, 17 Apr 2026 09:08:58 +0000

There's a particular kind of restlessness that comes from being a CS student in 2026.
It's not the assignments, or the deadlines, or even the exams. It's the feeling that by the time you've shipped something, the landscape has already shifted. Every week there's a new model, a new tool, a new framework that everyone on the internet insists will change everything. I've genuinely lost count of how many times I've read the words "the future is here" in the last six months alone.
So when OpenClaw started showing up everywhere — 20,000 GitHub stars in a day, people on every forum talking about their "personal AI agent" like it was a houseplant they were proud of — my first instinct was to actually look into it. I'd learned to tell the difference between hype and something worth digging into. This felt like the latter.
I was right.

Why I Actually Tried It

It wasn't the star count that got me. It was one specific thing I kept reading about: you run it yourself. On your own machine. Your data doesn't disappear into some company's server farm. Your conversations, your memory, your context — it all lives in plain text files that you can open, read, and edit like any other document.

That mattered to me more than I expected it to.

As someone who's spent four years studying how these systems work under the hood, I've grown increasingly uncomfortable with how much of my digital life runs inside black boxes I'm not allowed to inspect. Most AI tools are designed to keep you dependent and ignorant — not out of malice, but because that's the model. OpenClaw felt like a deliberate rejection of that. Not a subscription. Not a walled garden. A tool that actually respects the intelligence of the person using it.

The second pull was automation. I have a full plate — final year coursework, projects, part-time commitments, and a group chat that never sleeps. The idea that something could handle the repetitive edges of that without me having to babysit it wasn't a luxury. It was just practical.
So I set it up. It took an afternoon, a couple of wrong turns, and one moment where I was fairly sure I'd misconfigured something permanently. But I got there.

The First Week: Honest Notes from the Trenches

The first thing OpenClaw did that genuinely impressed me was remember something I told it on day one and surface it — unprompted — three days later in a completely different context. I already understood the mechanism: daily logs for short-term context, a long-term MEMORY.md file for the important stuff, all plain markdown. But seeing it work in practice still landed differently than reading about it. It felt less like a chatbot and more like a system that was actually tracking state in a meaningful way.

The automation side was where things got interesting from an engineering perspective. The first workflow I built was simple — summarising a long document and routing the output somewhere useful. The setup took longer than the time it saved, at first. But that's always how it goes when you're building infrastructure rather than just using it. Once the pattern clicks, everything after it gets faster.

What I didn't expect was how readable the internals would be. The memory files, the logs, the configuration — all of it is plain text you can inspect, version control, and reason about. For someone who's used to digging into source code to understand what a system is actually doing, that's not a small thing. It meant I could debug it like any other software rather than submitting a support ticket and hoping for the best.

What It Gets Right

Most AI tools are built on the assumption that you should trust them completely and ask no questions. You send input, you receive output, and the gap between the two is none of your business.

OpenClaw makes the opposite bet. The architecture is transparent by design. Your memory is in files you own. Your logs are yours to read. Your configuration is yours to modify. It's a system that treats you as someone capable of understanding what's happening — because you are.

That transparency has a compounding effect. Every time something didn't behave as expected and I had to dig into why, I came out with a clearer mental model of how AI agents actually work — the execution loops, the tool calls, the context management. That's knowledge that transfers. It doesn't matter what the next popular agent framework is; the underlying concepts are the same.

The other thing it gets right is low friction adoption. You don't need a new app or a new habit. You can interface with it through messaging platforms you already use. The best tools are the ones that fit into your life rather than demanding you reorganize around them. At its best, OpenClaw disappears into your existing workflow.

On the Pace of All This

I want to be honest about something: the pace of this space is genuinely wild — even when you're close to it. OpenClaw went from zero to the most-starred project in GitHub history in months. Its creator got hired by OpenAI mid-project. Serious security vulnerabilities, a thriving skill marketplace, a conference, major players building on top of it. All while I was finishing coursework.

Being a CS student doesn't make you immune to that pace — if anything, it makes you more aware of how much is happening simultaneously and how hard it is to separate signal from noise. The temptation is to try to follow everything. I've learned that's the wrong move. Better to go deep on something real than to skim the surface of everything.
OpenClaw has become that something for me — not because it's the most polished tool out there, but because it's open, it's honest about what it is, and working with it has been more educational than most things I've read about AI agents this year.

What Comes Next

I'm still early with it. There's a lot I haven't touched — deeper skill integrations, multi-agent setups, building on top of the API layer. But I'm not in a rush.

That might be the most useful reframe OpenClaw gave me: the goal isn't to keep up with everything. It's to actually build something, understand it properly, and carry that understanding forward regardless of what the landscape looks like next month.

If you're a developer who's been watching the AI agent space from a distance, waiting for something worth getting into — this is worth your time. Not because it'll automate your life overnight. But because understanding it changes how you think about everything else in this space.

And right now, that's the most useful thing a tool can do.