Kiwi

Inspiration

Kiwi AI was inspired by the sheer frustration of seeing AI developers struggle with deploying models efficiently. Whether it’s fine-tuning an LLM or optimizing a computer vision model, the process is expensive, time-consuming, and usually eats up a massive chunk of cloud budgets. Running a model on AWS? You could easily see hundreds of dollars in costs per month just from memory and compute. Why not fix that? We realized that many AI developers don’t want to spend time researching optimization techniques—they just want to deploy their models fast and cheaply, without sacrificing performance. Kiwi AI is our answer to that pain point.

What it does

In just one line of code, Kiwi AI optimizes any model—whether it’s LLMs, diffusers, NLP models, graphs, or audio models. Kiwi smashes the model by applying state-of-the-art methods like quantization, pruning, and algorithmic optimizations, reducing the memory usage and cutting inference costs significantly. It’s designed to handle all the complexity so developers don’t have to. With Kiwi, you get faster, smaller, and more efficient models without lifting a finger.

How we built it

We started by diving deep into the latest research on model compression and optimization techniques. The challenge was building a system that could automate all these processes across different model architectures and tasks. From Stable Diffusion to BERT to Graph Neural Networks, we developed Kiwi to adapt to any model type, making it as simple as possible to optimize for the user. It took multiple iterations to make it seamless, but we finally built an API that could scale across various domains without compromising model quality.

Challenges we ran into

One of the biggest challenges was preserving the quality of the models after optimization. Reducing inference time and memory usage is one thing, but keeping the model’s performance intact while “smashing” it with compression techniques is another. There were countless tests where optimization would introduce subtle errors or degrade performance. We had to fine-tune our methods to ensure that Kiwi balances both efficiency and accuracy.

Accomplishments that we're proud of

We’re most proud of Kiwi’s ability to reduce costs for developers by over 50% in some cases, without requiring them to become optimization experts. We also take pride in the fact that Kiwi can handle such a wide range of models—from generative AI to predictive analytics, all the way to NLP and computer vision. Seeing Kiwi seamlessly integrate into existing workflows and drastically improve efficiency has been one of the most rewarding aspects of the project.

What we learned

We learned that simplicity and usability are key when building tools for AI developers. The more we could automate and simplify the optimization process, the more valuable Kiwi became. We also realized that optimization isn’t a one-size-fits-all solution—each model and use case has its unique set of requirements. It was crucial to make Kiwi adaptable and customizable so developers could tweak it to their specific needs.

What's next for Kiwi

Next, we’re focusing on expanding Kiwi’s capabilities to support new hardware platforms and edge devices. We also plan to introduce more granular control, allowing developers to fine-tune their optimizations further while still keeping things simple. Ultimately, Kiwi’s mission is to make AI cheaper, faster, and cleaner for everyone, and we’re excited to keep pushing the boundaries of what’s possible.

Built With

• machine-learning
• optimization
• quant
• torch
• transformers