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MODEL · ENTITY #174

Nemotron 3.5 Lightning

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3

MODELS · 2 SOURCES · NVIDIA Developer · The Decoder

NVIDIA releases Nemotron 3.5 Super VL, a 120B-parameter open multimodal model for agents

NVIDIA announced Nemotron 3.5 Super VL, an open 120B-parameter mixture-of-experts multimodal model that activates 12B parameters per token to support agentic workflows combining reasoning, instruction following, tool use, and text, image, document, and video understanding. NVIDIA says it achieves up to 5× higher single‑GPU throughput on agentic tasks versus comparable open models, supports post‑training via NeMo (SFT, LoRA, RL) with resources on Hugging Face and a GitHub usage cookbook, and employs inference optimizations such as LiLiCorr speculative decoding and NVFP4 quantization; partners including Scale AI, Ramp, and AI21 report substantial cost, latency, or accuracy improvements in production workloads.

8.0

RESEARCH · 1 SOURCE · NVIDIA Developer

Agent evaluation shifts to executable end-to-end and step-level scoring; NVIDIA Nemotron 3.5 Lightning demonstrates the approach

Agent evaluation is moving from scoring individual function calls to running agents inside executable environments that track state across multi-step tool use, combining step-level (process) scoring to locate failures with end-to-end (outcome) scoring to verify final task completion; metrics roll up through a fixed Benchmark→Trial→Task→Turn→Step hierarchy and favor executable checks over reference- or LLM-judge methods. NVIDIA presents this framework and reports Nemotron 3.5 Lightning achieving 86% accuracy on PinchBench while completing tasks ~30% faster than comparable models, and recommends domain-specific, state-gated production evaluations; reproducibility docs and a demo on build.nvidia.com are provided.

7.0

MODELS · 1 SOURCE · NVIDIA Developer

Dense vs. MoE Models: Active Parameters, Throughput, and When to Choose Each

An NVIDIA Developer post compares dense and Mixture-of-Experts (MoE) architectures, showing how a 30B-parameter model can activate only about 3B parameters per token and discussing the resulting capacity and throughput trade-offs; Nemotron 3.5 Lightning is used as an illustrative example.

6.0