Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now
OpenAI’s long-awaited return to the “open” of its namesake occurred yesterday with the release of two new large language models (LLMs): gpt-oss-120B and gpt-oss-20B.
But despite achieving technical benchmarks on par with OpenAI’s other powerful proprietary AI model offerings, the broader AI developer and user community’s initial response has so far been all over the map. If this release were a movie premiering and being graded on Rotten Tomatoes, we’d be looking at a near 50% split, based on my observations.
First some background: OpenAI has released these two new text-only language models (no image generation or analysis) both under the permissive open source Apache 2.0 license — the first time since 2019 (before ChatGPT) that the company has done so with a cutting-edge language model.
The entire ChatGPT era of the last 2.7 years has so far been powered by proprietary or closed-source models, ones that OpenAI controlled and that users had to pay to access (or use a free tier subject to limits), with limited customizability and no way to run them offline or on private computing hardware.
AI Scaling Hits Its Limits Power caps, rising token costs, and inference delays are reshaping enterprise AI. Join our exclusive salon to discover how top teams are: Turning energy into a strategic advantage
Architecting efficient inference for real throughput gains
Unlocking competitive ROI with sustainable AI systems Secure your spot to stay ahead: https://bit.ly/4mwGngO
But that all changed thanks to the release of the pair of gpt-oss models yesterday, one larger and more powerful for use on a single Nvidia H100 GPU at say, a small or medium-sized enterprise’s data center or server farm, and an even smaller one that works on a single consumer laptop or desktop PC like the kind in your home office.
Of course, the models being so new, it’s taken several hours for the AI power user community to independently run and test them out on their own individual benchmarks (measurements) and tasks.
... continue reading