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Nemotron 3.5 Bolt 74B-A9B 24GB: New Hybrid Vision-Language Model for Local AI

Researchers have introduced Nemotron 3.5 Bolt 74B-A9B 24GB, a novel hybrid vision-language model designed for efficient local AI applications. This model, derived from the Nemotron 3.5 Super VL 120B-A12B family, utilizes Star Elastic compression and 2-bit Quantization-Aware Distillation to optimize performance within memory constraints. It demonstrates superior accuracy compared to existing models with similar memory and speed profiles across benchmarks for general agentic capabilities, web browsing, and terminal interactions. AI

IMPACT Optimizes local AI agentic workloads by balancing speed and intelligence with memory efficiency.

RANK_REASON The item is an arXiv preprint detailing a new model architecture and its performance benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

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Nemotron 3.5 Bolt 74B-A9B 24GB: New Hybrid Vision-Language Model for Local AI

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The item is an arXiv preprint detailing a new model architecture and its performance benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ganesh Ajjanagadde, Chris Alexiuk, Luke Alonso, Kumar Anshuman, Maor Ashkenazi, Nave Assaf, Gorkem Batmaz, Omer Belhasin, Shai Ben Nun, Akhiad Bercovich, Pritam Biswas, Caroline Blanco, Tijmen Blankevoort, Michael Boone, Christopher Borden, Kari Briski, … ·

    Nemotron 3.5 Bolt 74B-A9B 24GB: A Hybrid Vision-Language Model for Local AI

    arXiv:2610.33039v1 Announce Type: new Abstract: We introduce Nemotron 3.5 Bolt 74B-A9B 24GB, a hybrid Mamba-Transformer vision-language model that optimizes speed and intelligence per GB of memory used for local agentic workloads. Derived from the Nemotron 3.5 Super VL 120B-A12B …