[Submitted on 15 Oct 2026]
Title:Nemotron 3.5 Bolt 74B-A9B 24GB: A Hybrid Vision-Language Model for Local AI
et al. (9 additional authors not shown)
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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 model family, Nemotron 3.5 Bolt 74B-A9B 24GB combines Star Elastic compression with 2-bit Quantization-Aware Distillation for memory-constrained deployment. The model is optimized for reasoning, agentic tool use, and web & terminal interactions. We also trained a LiLiCorr speculative drafter that is released alongside the model to reach our target inference speed. The resulting model achieves a higher accuracy than leading models with similar memory footprints and speed across many benchmarks for general agentic capabilities (e.g., GDPVal v2), web browsing (e.g., BrowseComp), and terminal use (e.g., Terminal-Bench 2.1), with a relatively high TPS of 84 on RTX Spark that is comparable to models with much lower active parameter counts and more than sufficient for long-horizon agentic applications.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.33039 [cs.CV]
(or arXiv:2610.33039v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.33039
arXiv-issued DOI via DataCite
Submission history
From: Tijmen Blankevoort [view email] [v1] Thu, 15 Oct 2026 18:01:06 UTC (2,431 KB)
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