There’s been a lot of chatter about the “end of LLMs” or “LLMs starting to fail.” Those are only headlines – clickbait, really. If you dig a bit deeper, you’ll read that there are several schools of thought regarding how to efficiently scale the foundational models.
If the goal is AGI (artificial general intelligence, a term with no agreed-upon definition), then just adding compute power to pre-training may not be the best path to follow. Instead, researchers are exploring an alternative called “inference scaling” to achieve smarter AI. Inference, the process when an AI model generates outputs and answers, can be optimized by having models “think” through multiple possibilities before settling on a response. This approach enables complex reasoning during real-time use without increasing model size.
OpenAI’s recently launched o1 model is a good example. By enhancing inference, o1 can tackle tasks that demand layered decision-making, such as coding or problem-solving, in ways similar to human thought. “Test-time compute” techniques make this possible, allowing models to dedicate more processing to challenging queries as needed.
A move toward inference-focused, distributed, cloud-based servers (instead of large, centralized training clusters) might create a more competitive chip landscape. While NVIDIA is the go-to chipmaker for pre-training hardware, there are a bunch of chipmakers (AMD, Intel, etc.) that make hardware suitable for this new method of inference scaling.
The key takeaway is simple: LLMs are not failing, they are evolving. Sensationalist headlines aside, this is how product development works.
Author’s note: This is not a sponsored post. I am the author of this article and it expresses my own opinions. I am not, nor is my company, receiving compensation for it. This work was created with the assistance of various generative AI models.