NVIDIA Nemotron 3.5 Lightning 30B A3B BF16

NVIDIA Nemotron 3.5 Lightning 30B A3B BF16 is the full-precision release of Nemotron 3.5 Lightning, a hybrid Mixture-of-Experts language model trained by NVIDIA. It has 30 billion total parameters with only 3 billion active per token, and was released on August 11, 2026.

Nemotron is NVIDIA's family of open models — open weights, open training data, and open recipes — aimed at building specialized AI agents efficiently. These BF16 weights are the reference checkpoint for the model: the intended starting point for post-training (supervised fine-tuning, reinforcement learning, distillation), domain adaptation, producing your own quantized or GGUF variants, and full-precision research and evaluation.

Architecture

The model interleaves Mamba-2 state-space layers with Mixture-of-Experts layers and a small number of Attention layers. Of its 52 layers, 23 are Mamba-2, 23 are MoE, and 6 are Attention. That hybrid design is what keeps the active parameter count at 3 billion and lets the model hold very long contexts without the memory growth a pure attention stack would incur. It supports context lengths up to 1 million tokens, with 262,144 tokens configured as the native maximum position.

The model routes to 6 of 128 experts per token, alongside a shared expert, and ships with multi-token prediction layers plus two external draft models for speculative decoding.

Key features

• Hybrid Mamba-2, MoE and Attention architecture with 3B active parameters out of 30B total
• Configurable reasoning: thinking can be switched on or off through the chat template, and a runtime thinking budget can be set per request
• Native tool calling, with reasoning and tool-call output parsed into separate response fields
• Long-context support, evaluated up to very long inputs
• Multilingual: English and coding languages as primary, plus Spanish, French, German, Italian and Japanese
• Pre-training data through September 2025; post-training data through May 2026

Benchmarks

NVIDIA measured the model under a consistent internal harness, so numbers may differ from other vendors' self-reported results.

On general knowledge it scores 81.94 on MMLU Pro. On reasoning it reaches 75.44 on GPQA Diamond without tools and 32.60 on SciCode. On coding and agentic work it scores 51.56 on SWE-bench Verified, 39.33 on SWE-bench Multilingual, 24.58 on Terminal-Bench 2.1, 85.37 on PinchBench and 36.97 on BrowseComp. On instruction following it scores 71.88 on IFBench (loose), and on long-context retrieval it scores 52.00 on AA-LCR.

Against the other models NVIDIA compared it to, Lightning 30B-A3B leads its size class on PinchBench and BrowseComp, and outperforms the larger Nemotron 3 Nano across essentially the whole suite, while trailing Qwen 3.6 35B A3B on most coding and agentic tasks.

Use cases

• Post-training and domain adaptation on your own data
• Producing quantized or GGUF derivatives for your own deployment targets
• Agentic applications that need tool calling and controllable reasoning
• Long-document analysis, retrieval-augmented generation and summarization
• Research and evaluation at full precision

Sampling

NVIDIA recommends a temperature of 1.0 and top-p of 0.95.

Related releases

An inference-optimized quantized release is published separately as NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4, and the base (pre-instruction-tuning) weights as NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16.