New Google Gemma 4 Update is Insane (FREE!)

New Google Jemma 4 update is insane, and it's completely free. Google just made their free AI model 3 times faster. Not a little faster, 3 times. And the quality doesn't drop at all.

Same reasoning. Same accuracy. Just 3 times the speed. That's the update.

And it changes what's possible with local AI in a real way. Speed has always been the weak point of running AI on your own machine. You could have a genuinely powerful model sitting on your laptop, but the experience felt slow. Watching it generate one word at a time.

That friction is what kept most people on cloud tools. That friction is now gone. The update is called Multi-Token Prediction. MTP.

Let me explain it simply. Normal AI models predict one word at a time. Every single token, the big model does all the work. Over and over.

It's slow because nothing is running ahead. The model waits for itself. Google fixed that by adding a smaller, faster helper model. The helper predicts several words ahead at the same time.

The main model just checks whether those predictions are correct. Most of the time they are. When they're not, it corrects and moves on instantly. Picture a copilot reading the road ahead and calling out the turns while you drive.

You're not stopping to check the map every few seconds. You're just confirming and moving fast. Hey, if we haven't met already, I'm the digital avatar of Julian Goldie, CEO of SEO agency Goldie Agency. Whilst he's helping clients get more leads and customers, I'm here to help you get the latest AI updates.

Julian Goldie reads every comment, so make sure you comment below. On an NVIDIA RTX PRO 6000, Google showed the 26B model running at roughly double the tokens per second in a direct side-by-side comparison with standard inference. Same output, half the wait time. On the tiny E2B version built for phones, the jump is even bigger because Google added an extra optimization, a clustering trick that cuts out a bottleneck specific to edge models.

And here's the part that matters most. The E2B model needs about 1.5GB of RAM. That's your phone. A capable AI model running offline with no internet, no API key, no subscription.

The 26B MOE model fits on an RTX 3090 or a Mac with 24GB of unified memory. The 31B Dense sits at around 20GB for max quality. All four sizes now have MTP support. All four are free under Apache 2.0, meaning you can use them commercially, modify them and run them with no data leaving your device.

Before this update, running a powerful model locally was clunky for anything that needed to feel fast. Customer-facing tools, live content generation, agent workflows, the latency made it feel like a toy, not a real tool. That's shifted. Inside the AI Profit boardroom, we have 2,800 business owners actively automating with AI.

A lot of them are already running local model setups and building agent workflows for content, client work and lead generation. We've built a 30-day roadmap specifically around AI automation for business, including how to use free models like Jemma 4 so you're not locked into paying per API call or dependent on any one platform. Four coaching calls every week, daily tutorials, members deep in exactly this. Link in the description or go to aiprofitboardroom.com.

For the E2B and E4B phone models, Google added one more layer. Instead of scanning every possible word in the vocabulary for each prediction, it first figures out which cluster of similar words is most likely, then only looks inside that cluster. Less work, faster output, same quality. One thing worth knowing if you're on Apple Silicon.

At batch size one, one request at a time, the speed up is more limited because of how Apple Silicon handles routing in a mixture of experts model. But run it at batch sizes of four to eight, meaning processing a handful of tasks together and you get up to 2.2 times speed up. That's not a limitation, just something to build around. Jemma 4 already had 60 million downloads before MTP even launched.

When this update dropped, our local LLama had it running in LLama.cpp, Olimar, LM Studio and VLLM on the same afternoon. That kind of same day ecosystem support is rare. It tells you how much demand exists for exactly this open, fast, capable, free. The base model performance is strong too.

MMLU Pro at 85.2%, AIM 2026 at 89.2%. The 26B MOE scores 79.2% on GPQA Diamond, which puts it ahead of OpenAI's GPT OSS 120B, a model with 94 billion more parameters running on your laptop. Scoring lower. That gap is genuinely wild.

Now add three times faster inference on top of that. Jemma 4 also handles agentic tasks much better than the previous version. It can produce over 4,000 tokens of reasoning before locking in an answer. The 26B and 31B models support a 256K context window, meaning you can feed it an entire document, a long client conversation, or a full content brief, and it holds all of it in memory while it works.

For local AI agents that need to chain multiple steps together, that's a significant jump from where things were six months ago. Here's a practical example of what this means. Say you want an AI that reviews every piece of content before you publish it, checks it against your brand guidelines, your audience notes, and your best performing past work, and gives you a list of specific edits, running that locally offline with no API at three times the speed of the old Jemma. That workflow goes from something you'd avoid because it felt too slow to something you'd actually use every single day.

Or think about an AI layer that reads new client inquiries, sorts them by category, drafts the first response based on templates, and flags anything that needs human attention. All on your machine. No data going anywhere. No usage cap.

No cost per call. These are the workflows that weren't practical before. They are now. Zooming out, the AI race used to be about one thing.

Who has the biggest model? Most parameters. Highest benchmark. That race is still happening.

But a second race started alongside it, and it might matter more for most people. That second race is about efficiency. Getting more intelligence into smaller hardware. Running serious AI on devices people already own.

Google is doing this with Jemma. Meta is doing it with Llama. Alibaba is doing it with Quen. And right now with MTP, Jemma 4 has an inference speed advantage that neither Llama 4 nor Quen 3.5 currently match with an equivalent native update.

That matters because developers build on fast models. Businesses build products on fast models. The model that feels fastest in production gets embedded in more tools, more pipelines, more businesses. Once it's embedded, it stays embedded.

Before MTP was available, running a 7B model on an M2 MacBook sat at around 10 tokens per second. That's slow enough that you feel yourself waiting. Doubling or tripling that crosses a real threshold, where the model stops feeling like something you're waiting on and starts feeling like something you're actually working with. That's the line between a tool people tolerate and a tool people use every day.

Jemma 4 with MTP crosses that line on hardware most people already own. The trend is clear. Models are getting more capable at smaller sizes. Inference is getting faster.

What needed a data center two years ago runs on a desktop today. What runs on a desktop today will run on a phone in 18 months. Jemma 4 with MTP is a concrete step along that path. And it's a free one.

For anyone building with AI, automating business workflows, or trying to reduce dependence on expensive cloud APIs, this is worth paying attention to. A free, fast, capable model that runs offline, handles complex reasoning, supports 256k context, and works on hardware you already own is real business infrastructure, not a hobbyist experiment. The gap between cloud AI and local AI is closing faster than most people realize. And Google just pushed it closer.

If you want step-by-step tutorials on how to run Jemma 4 locally, build automated workflows around it, and use it to actually save time and get more done in your business, come join the AI Profit Boardroom. We've got daily tutorials covering tools exactly like this as they ship, four weekly coaching calls where we go deep on AI automation for business, a 30-day roadmap, 20,800 members running real AI systems right now, a lot of them already using local models for content, client delivery, and lead generation workflows. Link in the description or go to aiprofitboardroom.com And if you want the free version, notes from this video, SOPs and 100 plus AI use cases, including a full Jemma 4 setup guide, join the AI Success Lab. Free community, 67,000 members, all building with AI.

Links in the comments and description. The model is free. The speed is there. The only question is whether you start building with it now.