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. https://t.co/ENWrZe76pU
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Ant Ling has officially open-sourced Ling-3.0-tiny, a native hybrid reasoning model with 7.9B total parameters that activates only 1.3B parameters during inference, available in BF16, FP8, and INT4 versions. The company says it scores 25 on the Artificial Analysis Intelligence Index and 16 on the Agentic Index, with output speed exceeding 160 tokens/s. The model can run FP8 inference on a single NVIDIA DGX Spark with weights of about 7.85GB, and has been adapted for local execution on Apple Silicon MacBook and Mac mini.
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Today we're also opening the weights for Muse Glimmer, a great 30B parameter dense model that can run locally. Soon we'll also release the weights for Muse Spark 1.2, our latest foundation model. Meta is a strong supporter of open source and I'm proud of these releases. Congrats to @alexandr_wang and the MSL team for all your great work on these models.
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Fireworks has launched Meta's Muse Glimmer 30B dense model under the Apache 2.0 license, supporting 128K+ token context and native image understanding, targeting persistent agents that require multi-turn tool calling. The model scores 75.5 on MCP Atlas, 74.6 on DeepSearch QA, and 51.2 on SWE-Bench Pro, all higher than the corresponding results of Gemma 4 31B and Qwen 3.6 27B; these benchmark figures are reported by Meta. Fireworks also provides recommended settings of temperature 1.0, top_p 0.95, and top_k 64, and supports adjusting reasoning intensity in the system prompt.
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Ant Ling has open-sourced Ling-3.0-flash, a new-generation native hybrid reasoning model built on a MoE architecture with 124B total parameters and 5.1B activated parameters, and is simultaneously releasing a base version along with multiple precision versions including FP8, FP4, and INT4. The model supports three deployment approaches: cloud API calls, end-to-end local inference on a single NVIDIA DGX Spark using the MXFP4 or INT4 version, and high-performance deployment achieving an average output rate exceeding 1100 tokens/s under designated GPU test configurations. On the Artificial Analysis leaderboard, its output speed is 353 tokens/s, and in the AA Intelligence Index, the weighted average invocation cost per task is approximately $0.04.
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