Mistral AI, a rising star in the artificial intelligence arena, launched two new language models on Wednesday, potentially reshaping how businesses and developers deploy AI technology.

The Paris-based startup’s new offerings, Ministral 3B and Ministral 8B, are designed to bring powerful AI capabilities to edge devices, marking a significant shift from the cloud-centric approach that has dominated the industry.

These compact models, collectively dubbed “les Ministraux,” are surprisingly capable despite their small size. Ministral 3B, with just 3 billion parameters, outperforms Mistral’s original 7 billion parameter model on most benchmarks. Its larger sibling, Ministral 8B, boasts performance rivaling models several times its size.

Edge AI: Bringing intelligence closer to users

The significance of this release extends far beyond technical specifications. By enabling AI to run efficiently on smartphones, laptops, and IoT devices, Mistral is opening doors to applications previously considered impractical due to connectivity or privacy constraints.

This shift towards edge computing could make advanced AI capabilities more accessible, bringing them closer to end-users and addressing privacy concerns associated with cloud-based solutions.

Consider a scenario where a factory robot needs to make split-second decisions based on visual input. Traditionally, this would require sending data to a cloud server for processing, introducing latency and potential security risks. With Ministral models, the AI can run directly on the robot, enabling real-time decision-making without external dependencies.

This edge-first approach also has profound implications for personal privacy. Running AI models locally on devices means sensitive data never leaves the user’s possession.

This could significantly impact applications in healthcare, finance, and other sectors where data privacy is paramount. It represents a fundamental shift in how we think about AI deployment, potentially alleviating concerns about data breaches and unauthorized access that have plagued cloud-based systems.

Balancing efficiency and environmental impact

Mistral’s timing aligns with growing concerns about AI’s environmental impact. Large language models typically require significant computational resources, contributing to increased energy consumption.

By offering more efficient alternatives, Mistral is positioning itself as an environmentally conscious choice in the AI market. This move aligns with a broader industry trend towards sustainable computing, potentially influencing how companies approach their AI strategies in the face of growing climate concerns.

The company’s business model is equally noteworthy. While making Ministral 8B available for research purposes, Mistral is offering both models through its cloud platform for commercial use.

This hybrid approach mirrors successful strategies in the open-source software world, fostering community engagement while maintaining revenue streams.

By nurturing a developer ecosystem around their models, Mistral is creating a robust foundation against larger competitors, a strategy that has proven effective for companies like Red Hat in the Linux space.

Navigating challenges in a competitive landscape

The AI landscape is becoming increasingly crowded. Tech giants like Google and Meta have released their own compact models, while OpenAI continues to dominate headlines with its GPT series.

Mistral’s focus on edge computing could carve out a distinct niche in this competitive field. The company’s approach suggests a future where AI is not just a cloud-based service, but an integral part of every device, fundamentally changing how we interact with technology.

However, challenges remain. Deploying AI at the edge introduces new complexities in model management, version control, and security. Enterprises will need robust tooling and support to effectively manage a fleet of edge AI devices.

This shift could spawn an entirely new industry focused on edge AI management and security, similar to how the rise of cloud computing gave birth to a plethora of cloud management startups.

Mistral seems aware of these challenges. The company is positioning its new models as complementary to larger, cloud-based systems. This approach allows for flexible architectures where edge devices handle routine tasks, while more complex queries are routed to more powerful models in the cloud. It’s a pragmatic strategy that acknowledges the current limitations of edge computing while still pushing the boundaries of what’s possible.

The technical innovations behind les Ministraux are equally impressive. Ministral 8B employs a novel “interleaved sliding-window attention” mechanism, allowing it to process long sequences of text more efficiently than traditional models.

Both models support context lengths of up to 128,000 tokens, translating to about 100 pages of text—a feature that could be particularly useful for document analysis and summarization tasks. These advancements represent a leap forward in making large language models more accessible and practical for everyday use.

As businesses grapple with the implications of this technology, several key questions emerge. How will edge AI impact existing cloud infrastructure investments? What new applications will become possible with always-available, privacy-preserving AI? How will regulatory frameworks adapt to a world where AI processing is decentralized? The answers to these questions will likely shape the trajectory of the AI industry in the coming years.

Mistral’s release of compact, high-performing AI models signals more than just a technical evolution—it’s a bold reimagining of how AI will function in the very near future.

This move could disrupt traditional cloud-based AI infrastructures, forcing tech giants to rethink their dependence on centralized systems. The real question is: in a world where AI is everywhere, will the cloud still matter?

Do you want a side of socials with your fast food? Spain’s Glovo, a food delivery and quick commerce service where the bulk of orders are for ready-to-eat food, is experimenting with adding a bundle of social features to drive more in-app activity.

It’s also letting restaurant partners upload food preparation videos to spice up the user experience by being able to push a feed of rich media at its users.

Glovo says the new social features will let users find and connect with friends who are also using the app. This is powered by matching a user’s phone contacts to existing Glovo users and sending permission requests to connect. It enables users who opt in to be able to share recommendations and discover restaurants their friends have liked.

Another new feature called “Picks” lets Glovo users save and organize their favorite restaurants by creating and managing lists. These picks/lists can be private or shareable with friends.

For the food videos feature, the app is replicating the vertical feed that’s popular on social media apps like TikTok to give restaurants a new marketing opportunity — letting them display a showreel of stuff on their menu.

Glovo is not producing any of the food videos itself but does apply guidelines to submissions from restaurants, which it says are intended to ensure high quality (it says it’s had close to 400 submissions so far). “The content is strictly focused on the food offered, and other types of promotions are not allowed,” it noted.

The app updates, which Glovo previewed at a new annual product event called Glovo Next, will go live in its home city of Barcelona first before being rolled out to other cities in Spain this month. If all goes to plan, the company says it will keep expanding the rollout to reach the other 22 countries where it operates.

Food decisions made even easier?

While the quick commerce category in general has been through a bumpy ride over the last few years, undergoing something of a post-pandemic correction, Glovo — which is owned by Germany’s Delivery Hero — denies the new updates are a response to any flagging consumer interest in its delivery app.

“Glovo shows an outstanding GMV [Gross Merchandise Volume] growth trajectory (+44% FY2022 versus FY2024) with strong performance across all countries,” Daniel Alonso, Glovo’s vice president product, told TechCrunch.

Adjusted EBITDA is expected to improve by around 10 percentage points compared to how the company was operating before its late 2021 acquisition by Delivery Hero, he also said — crediting this to continued growth in profitable markets and to “less mature markets” scaling up.

Glovo is expected to reach positive adjusted EBITDA in the second half of this year, Alonso added.

The company also told us its quick commerce division — which offers speedy deliveries of grocery items, flowers, books, pet supplies, toiletries, and electronics picked out of stock ranged at its city center-located dark stores — is growing at over 50% year-over-year.

So if growth on its straightforward, transactional app is strong why has it decided to experiment with layering social media stickiness on top? Alonso says this is about Glovo continuing its brand building efforts and looking for ways to help users with the tricky decision of what meal to order in tonight.

The company said it hopes the features will boost food discoverability, including by tapping into the pull of friend-based recommendations. “We believe that building a social network inside Glovo will help users to make food ordering decisions and more,” said Alonso. “We have built an MVP to see whether this is the case, and time will tell if this is going to be successful.”

“Deciding what to order can be overwhelming with so many options,” he added. “The video discovery wall adds a dynamic, visual element to food exploration, making the process easier and more engaging. These videos, created by local restaurants, allow users to see how dishes are prepared and what they look like, helping them make more informed choices.”

On the permissions and privacy side, Alonso claimed: “Glovo does not share or upload any data without explicit consent.”

He said a user must actively grant access to their contact list in order to add friends — who must themselves provide express consent to connecting with the requested user before any information about their food delivery habits gets shared with friends.

“In order to see which of your friends have rated positively or have placed an order, you need to ensure that they have become your Glovo friends,” he noted, adding: “The shared data includes restaurants the user’s friends have ordered from in the last three months.”

Glovo users are able to revoke consent previously given to accessing their contacts, per Alonso, and can remove friends from their social list at any time after they’ve been added.

Additionally, Alonso confirmed that only contacts who are already Glovo users can receive an invitation to connect, so this isn’t that particular (notorious) growth hacking trick.