Andrej Karpathy, a former OpenAI researcher and Tesla’s former director of AI, calls his latest project the “best ChatGPT $100 can buy.”
Called “nanochat,” the open-source project, released yesterday for his AI education startup, EurekaAI, shows how anyone with a single GPU server and about $100 can build their own mini-ChatGPT that can answer simple questions and write stories and poems.
With fewer parameters, these small models don’t try to match the power of frontier models like GPT-5, Claude, and Gemini. But they are good enough for specific tasks, affordable to train, lightweight enough to use on devices like phones and laptops, and easy for startups, researchers, and hobbyists to build and deploy.
Much of this momentum traces back to China’s DeepSeek, whose lean, low-cost models upended industry assumptions at the beginning of this year and kicked off a race to make AI smaller, faster, and smarter. But it’s important to note that these models, while impressive, aren’t designed to match the broad capabilities of frontier systems like GPT-5. Instead, they’re built for narrower, specialized tasks—and often shine in specific use cases.
For example, this week IBM Research, along with NASA and others, released open-source, “drastically smaller” versions of its Prithvi and TerraMind Earth-observation models that can run on almost any device, from satellites orbiting Earth to the smartphone in your pocket, all while maintaining strong performance. “These models could reshape how we think about doing science in regions far from the lab—whether that’s in the vacuum of space or the savanna,” the company wrote in a blog post.
None of this means the era of massive, trillion-parameter models is coming to an end. As companies like OpenAI, Google, and Anthropic push for artificial general intelligence, which requires more reasoning capabilities, those will be the models that push the frontier. But the rise of smaller, cheaper, and more efficient models shows that AI’s future won’t be defined by size alone.
