Together AI, a company that rents out computing power for building and running AI models, lowered the price of fine-tuning, the process of further training an existing model on a company's own data, by 30 to 70 percent across most of the models it supports. For one model, OpenAI's open-weight gpt-oss-20b, the standard training price fell from 1.50 dollars to 0.40 dollars per million tokens processed. Together AI says the reduction comes from its own optimisation of the systems that do this training, not from a change in what the underlying chips cost. For a software team or startup that fine-tunes models to fit its own product, this is a direct reduction in one of the recurring costs of building an AI product, though it applies to training a model rather than running it once it is finished.
Together AI ported its open source ThunderKittens programming tools to Nvidia's newest chip, called Vera Rubin NVL72, and says it reached over 22 petaflops of real-world performance per GPU, a measure of raw calculation speed, competitive with Nvidia's own closed-source software for the same job. A petaflop is a thousand trillion calculations per second; Nvidia's previous generation chip, the B200, tops out at 9 petaflops per GPU on this kind of workload on paper, so Vera Rubin represents a large jump in raw hardware capability. This is Together AI's own benchmark of its own open source code, not independently verified, and it is a technical achievement inside the AI infrastructure world rather than a product change that affects prices today. For anyone using an AI product, tools like this are part of what eventually lets AI companies use new chips at close to their full speed once they are available.
Together AI, a cloud company that rents out GPU computing power, launched a new pricing tier called preemptible compute, priced at a flat 50 percent of its regular on-demand rate. The tradeoff is that Together can reclaim the computer at short notice, giving five minutes' warning before it does. This is a new, cheaper option alongside Together's existing prices, not a cut to what existing customers already pay. For a developer renting GPU computing power to train or run an AI model, this is a real way to cut costs, if the work can tolerate being interrupted.