Nvidia ships Nemotron 3.5 Lightning and an open model router
The enterprise AI story today is about choice: Nvidia shipped a fast, easily customized open model plus a router that picks between models, IBM bet $240 million on open-model inference, and Spotify decided AI-generated artist identities shouldn't get recommendations.
Nvidia released Nemotron 3.5 Lightning, a 30-billion-parameter mixture-of-experts model aimed at the high-volume, always-on workloads that power AI agents, alongside NeMo Switchyard, an open-source library that routes each step of an agent workflow to the best model for the job. Nvidia claims Lightning delivers up to four times the output speed and 30 percent faster agentic task completion than other models in its weight class — and, more importantly, it's built to be specialized fast. The company is shipping the post-training datasets and recipes it used to train the model, so an enterprise can re-tune Lightning on its own hardware and domain data; Nvidia's VP of generative AI cited one partner that produced a router agent for $85 in about two hours using the standard recipe, and another that dropped the model into an existing post-training stack with no changes.
The router half matters as much as the model. Switchyard, released under Apache 2.0, picks between available models at every step of an agent run based on quality, cost, or latency priorities — the "which model, when" problem every serious agent team is hitting as model options multiply. Partners already testing it include Cognition, LangChain, Kong, Siemens and Nous Research. Nvidia's play is subtle: instead of pushing one model for everything, it's making itself the default layer for choosing among many — a tollbooth on the agent economy.
IBM and Together AI signed a multi-year, $240 million agreement to deploy a large-scale AI inference cluster on IBM Cloud, using Nvidia's HGX B300 systems and Spectrum-X Ethernet networking, with availability expected in Q1 2027. Together AI will run open-source model inference on the cluster — the first dedicated, large-scale inference deployment of its kind on IBM Cloud — giving enterprises a hybrid path to serving open models without standing up their own GPU fleets. It's the latest sign that inference, not training, is where the cloud providers are now fighting: IBM is effectively renting Together AI's software stack and open-model credibility to compete with the bigger clouds' managed inference offerings.
Spotify will label artist profiles that represent AI-generated identities with "AI Persona" badges and, by default, exclude their music from editorial, algorithmic and personalized recommendations. Badges start appearing in mid-September, and Spotify says it won't rely on self-disclosure alone: it will review profiles — starting with the most-listened — and flag those whose name and imagery suggest a photorealistic AI identity. Users who explicitly follow an AI Persona will still hear their music, artists can appeal a label, and a user-reporting tool is on the way. The badge judges identity, not process — how the music was made stays covered by existing features like AI Credits. It's Spotify's most concrete answer yet to the AI-slop deluge, and a signal that platforms are moving from labeling content to labeling the entities behind it.
What to watch: whether rivals copy Spotify's identity-based approach — and how its AI remix and cover licensing deals with UMG and Merlin sit alongside it.
Should platforms demote AI-generated artists by default, or does that punish creators who use AI as a tool? Tell us in the comments.
Sources: NeMo Switchyard (GitHub)
