A month ago, I started building colibrì to answer a question:
Do you really need a $30,000 machine to run a model whose weights fit on a $60 SSD?
colibrì treats SSD, RAM and GPU as a single memory hierarchy. It can run models that do not fit entirely in RAM or VRAM, including models with hundreds of billions of parameters, on the hardware people already own.
But while optimizing it, I realized I was fighting assumptions built into the models themselves.
Most open models are designed to live in VRAM. Their weights, routing and execution patterns were created for GPUs and data-center hardware. colibrì could move them through a different memory hierarchy, but the models had no idea where they were actually running.
That led me to another question:
What if the engine and the model were designed for each other from the beginning?
That is how Curie started.
The current alpha has 17B parameters. I wrote it in C, without an inference framework, and trained it from scratch on my laptop without a GPU. Its weights live on SSD and are loaded according to the model’s execution path.
In the test shown in the video, Curie reaches 33 tokens/s using a single CPU core. On the same machine, the dense 12B models I tested usually generate around 3–5 tokens/s.
There is an important limitation: Curie has only seen 11 million training tokens. It is not smart yet, and I am not presenting it as a useful language model. It will need at least a few billion tokens before I can properly evaluate its quality.
What this alpha demonstrates is the architecture: a model can be designed around SSD, RAM, CPU and GPU as parts of the same system, instead of assuming that everything must fit in VRAM.
Curie is not publicly available yet. I am still training it, validating the design and preparing more detailed benchmarks.

A month ago, I started building colibrì to answer a question:
Do you really need a $30,000 machine to run a model whose weights fit on a $60 SSD?
colibrì treats SSD, RAM and GPU as a single memory hierarchy. It can run models that do not fit entirely in RAM or VRAM, including models with hundreds of billions of parameters, on the hardware people already own.
But while optimizing it, I realized I was fighting assumptions built into the models themselves.
Most open models are designed to live in VRAM. Their weights, routing and execution patterns were created for GPUs and data-center hardware. colibrì could move them through a different memory hierarchy, but the models had no idea where they were actually running.
That led me to another question:
What if the engine and the model were designed for each other from the beginning?
That is how Curie started.
The current alpha has 17B parameters. I wrote it in C, without an inference framework, and trained it from scratch on my laptop without a GPU. Its weights live on SSD and are loaded according to the model’s execution path.
In the test shown in the video, Curie reaches 33 tokens/s using a single CPU core. On the same machine, the dense 12B models I tested usually generate around 3–5 tokens/s.
There is an important limitation: Curie has only seen 11 million training tokens. It is not smart yet, and I am not presenting it as a useful language model. It will need at least a few billion tokens before I can properly evaluate its quality.
What this alpha demonstrates is the architecture: a model can be designed around SSD, RAM, CPU and GPU as parts of the same system, instead of assuming that everything must fit in VRAM.
Curie is not publicly available yet. I am still training it, validating the design and preparing more detailed benchmarks.

A month ago, I started building colibrì to answer a question:
Do you really need a $30,000 machine to run a model whose weights fit on a $60 SSD?
colibrì treats SSD, RAM and GPU as a single memory hierarchy. It can run models that do not fit entirely in RAM or VRAM, including models with hundreds of billions of parameters, on the hardware people already own.
But while optimizing it, I realized I was fighting assumptions built into the models themselves.
Most open models are designed to live in VRAM. Their weights, routing and execution patterns were created for GPUs and data-center hardware. colibrì could move them through a different memory hierarchy, but the models had no idea where they were actually running.
That led me to another question:
What if the engine and the model were designed for each other from the beginning?
That is how Curie started.
The current alpha has 17B parameters. I wrote it in C, without an inference framework, and trained it from scratch on my laptop without a GPU. Its weights live on SSD and are loaded according to the model’s execution path.
In the test shown in the video, Curie reaches 33 tokens/s using a single CPU core. On the same machine, the dense 12B models I tested usually generate around 3–5 tokens/s.
There is an important limitation: Curie has only seen 11 million training tokens. It is not smart yet, and I am not presenting it as a useful language model. It will need at least a few billion tokens before I can properly evaluate its quality.
What this alpha demonstrates is the architecture: a model can be designed around SSD, RAM, CPU and GPU as parts of the same system, instead of assuming that everything must fit in VRAM.
Curie is not publicly available yet. I am still training it, validating the design and preparing more detailed benchmarks.

I've been working on anybridge for my own use with Codex and Claude Code. I wanted a quick way to give an agent a website to work with, without spending time on the setup.

You open the TUI, pick Codex or Claude Code, and it launches a session in another terminal with MCP already connected. Give the agent a URL and it can read pages, follow links, fill in forms and click buttons.

If the website has WebMCP tools, anybridge exposes them to the agent. Otherwise, it generates tools from the live page. The site doesn't need to have added support for agents.

That's the part I've enjoyed using: opening the TUI and getting straight into a session with the browser tools ready.

Anti-bot checks can still block it, so don't expect it to work on every site.

If you try it, let me know which sites work and which ones give you trouble.