This architecture allows the model to deliver the reasoning and knowledge capacity of a much larger dense model while achieving the inference speed and lower computational cost of a smaller model.
Will it’s implementation across industries continue to reduce the current workforce ??
Can any knowledgeable programming/computer experts weigh in on the pro/cons of this jump in computing ease?
Introducing NVIDIA Nemotron 3.5 Lightning⚡
An open 30B MoE model with 3B active parameters, built for always-on agents to complete high-volume, specialized tasks faster.
It delivers up to 4x the output speed of similar-sized models. pic.twitter.com/ENWrZe76pU
— NVIDIA AI (@NVIDIAAI) August 11, 2026
Introducing NVIDIA Nemotron 3.5 Lightning
An open 30B MoE model with 3B active parameters, built for always-on agents to complete high-volume, specialized tasks faster.
It delivers up to 4x the output speed of similar-sized models.
Introducing Muse Glimmer, an open-weight 30B-parameter model optimized for local, always-on agent workflows.
Muse Glimmer delivers strong performance on key agentic use cases and benchmarks compared with leading models in its size category, and is designed to run entirely on… pic.twitter.com/mI4z91GPnE
— AI at Meta (@AIatMeta) August 10, 2026
Introducing Muse Glimmer, an open-weight 30B-parameter model optimized for local, always-on agent workflows.
Muse Glimmer delivers strong performance on key agentic use cases and benchmarks compared with leading models in its size category, and is designed to run entirely on consumer hardware like a Mac or PCs with performant GPUs.
In keeping with our long tradition of sharing fundamental AI research, we’re releasing model weights under a permissive Apache 2.0 license.
h/t KeepIt