nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16

NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16


Model Summary

| | | |:---|:---| | Total Parameters | 30B (3B active) | | Architecture | MoE — Mamba-2 + MoE + Attention hybrid | | Precision | BF16 (full-precision reference weights) | | Context Length | Up to 1M tokens (for single H100 deployment, we use 256K) | | Single-GPU Deployment | 1× H100 80GB (or 1× A100 80GB) | | Supported Hardware | NVIDIA Blackwell (GB200, GeForce RTX 5090); NVIDIA Hopper (H100, H200); NVIDIA Ampere (A100) | | Supported Languages | English (and coding languages), Spanish, French, German, Italian, Japanese | | Speculative Decoding | DSpark for Low Concurrency Data Centre Deployments — [Read more below](#speculative-decoding-strategies) | | Reasoning Mode | Configurable on/off via chat template (enable_thinking=True/False)| | Recommended Sampling | Temperature 1.0, Top_P 0.95 | | Best For | Customization — post-training (SFT, RL, distillation), domain adaptation, building quantized variants, and research/evaluation at full precision | | Looking to Deploy? | For optimized inference, see NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 | | License | OpenMDW License Agreement, version 1.1 | | Release Date | August 11, 2026 |

Model Overview

Model Developer: NVIDIA Corporation

Model Dates: December 2025 - May 2026

Data Freshness:

• The pre-training data has a cutoff date of September 2025.
• The post-training data has a cutoff date of May 2026.

What is Nemotron?

NVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents.

Description

NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 is a large language model (LLM) trained by NVIDIA. This is the full-precision (BF16) release of Nemotron 3.5 Lightning — the reference weights of the model, intended primarily as the starting point for customization: post-training (SFT, RL, distillation), domain adaptation, and producing your own quantized or GGUF variants. For latency- and throughput-optimized inference, use the NVFP4 release instead.

The model employs a hybrid Mixture-of-Experts architecture, utilizing interleaved Mamba-2 and MoE layers, along with select Attention layers. The Lightning 3.5 model is released alongside a number of speculative decoding methods for faster text generation. The model has 3B active parameters and 30B parameters in total.

This model is ready for commercial use.

Quick Start

> *For running Nemotron 3.5 Lightning fast — with NVFP4 quantization, W4A16 for broad hardware coverage, and the DSpark recipe for DGX Spark — please see: NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4*

To get quickly started on a single H100 you can use the following command.

Grab the model:

export MODEL_CKPT=nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16

Run it with vLLM! (vLLM Nightly: vllm/vllm-openai:v0.27.1)

vllm serve --model $MODEL_CKPT \
--max-num-seqs 128 \
--enable-prefix-caching \
--async-scheduling \
--mamba-backend flashinfer \
--mamba-ssm-cache-dtype float16 \
--enable-mamba-cache-stochastic-rounding \
--mamba-cache-philox-rounds 5

For more details on how to deploy and use the model — see the [Quick Start Guide](#quick-start-guide) below!

License/Terms of Use

Governing Download Terms: Use of this model is governed by the OpenMDW-1.1 model license.

Benchmarks

Reasoning Benchmark Evaluations

We evaluated our model on the following benchmarks:

| Task | Nemotron-3.5-Lightning-30B-A3B-BF16 | Qwen 3.6 35B A3B | Gemma 4 26B A4B | Nemotron 3 Nano | Nemotron 3 Super | GPT-OSS 20B | | --- | --- | --- | --- | --- | --- | --- | | General Knowledge | | | | | | | | MMLU Pro | 81.94 | 85.63 | 85.20 | 78.46 | 83.89 | 76.40 | | AA-Omniscience | 17.50 | 19.47 | 22.17 | 20.15 | 26.68 | 16.62 | | Reasoning | | | | | | | | GPQA Diamond (no tools) | 75.44 | 83.40 | 79.61 | 74.05 | 78.60 | 71.46 | | HLE (text-only, no tools) | 11.72 | 19.56 | 17.42 | 10.89 | 20.30 | 13.76 | | SciCode | 32.60 | 35.33 | 40.28 | 30.08 | 35.11 | 38.63 | | Coding & Agentic | | | | | | | | SWE-bench Verified | 51.56 | 70.12 | 57.40 | 34.08 | 63.08 | 52.44 | | SWE-bench Multilingual | 39.33 | 63.40 | 43.40 | 14.07 | 49.80 | 41.93 | | Terminal-Bench 2.1 | 24.58 | 44.38 | 37.22 | 8.29 | 39.61 | 15.17 | | PinchBench | 85.37 | 88.07 | 74.70 | 66.11 | 80.36 | 57.20 | | BrowseComp | 36.97 | 48.74 | 26.30 | 13.74 | 22.77 | – | | τ³-bench (Banking) | 9.28 | 10.52 | 14.02 | 7.01 | 12.37 | – | | GDPval-AA-V2 | 832 | 1015 | 807 | 473 | 746 | – | | Instruction Following | | | | | | | | IFBench (loose) | 71.88 | 63.71 | 77.25 | 72.17 | 71.92 | 68.50 | | Long Context | | | | | | | | AA-LCR | 52.00 | 61.06 | 57.56 | 32.75 | 58.44 | 32.88 |

Accuracy numbers measured by NVIDIA under a consistent harness (NeMo Gym / Nemo Evaluator SDK); they may differ from vendors' self-reported numbers.

For reproducibility, the evaluation recipes, installation instructions, and commands for NVIDIA Nemotron 3.5 Lightning were collected and published in NeMo Gym. The reported results cover the release evaluation suite, including knowledge and reasoning, instruction following, coding, agentic, tool-use, and long-context. Most evaluations use NeMo Gym-native harnesses while a small subset, including SWE-Bench and Terminal-Bench, used NeMo Evaluator natively. The published recipes specify the benchmark-specific containers, prompts, inference parameters, parser configurations, and scoring settings used to produce the results.

Agentic Coding Benchmarks

Additional harness-level coding-agent results for SWE-Bench Verified and Terminal-Bench 2.1 are shown below.


Deployment Geography: Global

Use Case...