SiMa.ai Launches New Chip to Run LLMs on Devices in 2025

Artificial intelligence has been racing forward, and every few months, we hear about new chips designed to power the next wave of breakthroughs. But the latest news from SiMa.ai is catching extra attention: the company has launched a new chip that can run large language models (LLMs) directly on devices—all while consuming less than 10 watts of power.

For anyone following AI trends in 2025, this is a big deal. Running LLMs locally (instead of relying on massive data centers) opens the door to faster, safer, and more efficient AI. So, what exactly has SiMa.ai built, and why does it matter? Let’s break it down.

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The Chip Everyone’s Talking About

SiMa.ai calls its new creation the Modalix MLSoC—a high-performance, low-power system-on-chip that’s designed specifically for edge AI. Unlike cloud-based AI infrastructure, which requires heavy servers and endless power, this chip is all about efficiency without compromise.

The Modalix MLSoC is already in production and is available as both a System-on-Module (SoM) and a Development Kit. Prices start at $349 for the 8GB module and go up to $1,499 for the DevKit. Compared to traditional GPU-based modules, this solution is drop-in compatible, which means developers can switch with minimal hassle.

Why It Matters for AI in 2025

The timing couldn’t be better. AI is everywhere in 2025—powering everything from chatbots and autonomous vehicles to medical diagnostics and robotics. But the problem has always been cost and energy demand.

Training and deploying LLMs like GPT requires enormous server farms, which burn through power like there’s no tomorrow. That’s fine for tech giants with unlimited budgets, but what about companies building robots, drones, or medical devices?

This is where SiMa.ai’s new chip shines:

It can handle transformers, vision-language models, and CNNs on-device.
It consumes less than 10 watts, a fraction of what GPUs require.
It allows real-time AI without depending on an internet connection.

LLiMa: The Secret Sauce

Hardware is only half the story. Alongside the Modalix MLSoC, SiMa.ai is rolling out LLiMa™, a software framework that simplifies deploying LLMs on edge devices.

Think of it as a “translator” that takes complex AI models, optimizes them, and makes them run efficiently on SiMa.ai’s hardware. From quantization to compilation to runtime optimization, LLiMa handles the heavy lifting. For developers, this means less coding frustration and faster AI integration.

Comparing with the Competition

Of course, SiMa.ai isn’t the only player in this game. Nvidia, Qualcomm, and even Apple have been pushing hard on low-power AI chips. Nvidia dominates the high-performance AI training market, while Qualcomm and Apple focus on mobile AI efficiency.

But here’s where SiMa.ai stands out:

Laser focus on edge AI. Instead of trying to be everything to everyone, SiMa.ai is targeting robotics, automotive, smart vision, and embedded systems.
Low entry cost. At under $600 for a high-memory SoM, it’s accessible to startups and researchers.
Drop-in compatibility. Developers already using GPU modules can adopt SiMa.ai’s solution with minimal redesign.

In short, while the big players chase massive data centers, SiMa.ai is betting on a “physical AI” future—where intelligence runs locally, everywhere.

The Future Possibilities

The possibilities are fascinating. Imagine a world where:

Robots in factories run LLMs without needing cloud servers.
Medical devices provide instant AI analysis, even in remote areas.
Self-driving cars process AI decisions on the road without lag.
Smart cameras and drones deliver real-time AI insights while staying lightweight and efficient.

This is the vision SiMa.ai is pushing forward, and if it succeeds, it could reshape how we think about AI infrastructure.

Challenges Ahead

Of course, it’s not all smooth sailing. Running LLMs on-device is still new, and challenges remain:

Model size: The largest LLMs still require more memory and compute than an edge chip can handle.
Ecosystem adoption: Developers need time to embrace a new platform.
Competition: Giants like Nvidia aren’t going to let go of their market share without a fight.

Still, the fact that a startup like SiMa.ai is pushing the boundaries is exciting—and it adds much-needed diversity to the AI hardware landscape.

Wrapping Up

The SiMa.ai Modalix MLSoC launch in 2025 isn’t just another chip release—it’s a signal of where the AI world is heading. On-device intelligence, powered by efficient and affordable hardware, could be the next big leap after cloud AI.

By focusing on low power, high performance, and real-world applications, SiMa.ai has positioned itself as a serious challenger in the AI race. Whether you’re an investor, a developer, or simply an AI enthusiast, this launch is worth paying attention to. The AI future isn’t just in the cloud anymore—it might be sitting inside the devices around you.