nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 · Hugging Face

NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 Model Summary Total Parameters 30B (3B active) Architecture MoE - Mamba-2 + MoE + Attention hybrid Context Length Up to 1M tokens Single-GPU Deployment 1× DGX Spark (GB10) or 1× H100 Supported Hardware NVIDIA Blackwell (DGX Spark / GB10, GB200, GeForce RTX 5090); NVIDIA Hopper (H100, H200); NVIDIA Ampere via W4A16 Supported Languages English (and coding languages), Spanish, French, German, Italian, Japanese Speculative Decoding DSpark for low-concurrency Data Centre and DGX Spark Workflows — Read more below, also provided are MTP (Multi-Token Prediction) and DFlash Recommended Sampling Temperature 1.0, Top_P 0.95 Best For Long-running autonomous agents, sub-agent workhorse deployments, and efficient local inference on personal hardware 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-NVFP4 is a large language model (LLM) trained by NVIDIA. 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 To get quickly started on DGX Spark (GB10) you can use the following command. Grab the model: export MODEL_CKPT=nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 export DSPARK_CKPT=nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4-DSpark Run it with vLLM — this recipe uses DSpark speculative decoding, tuned for DGX Spark. (vLLM Nightly: vllm/vllm-openai:v0.27.1) vllm serve --model $MODEL_CKPT \ --moe-backend marlin \ --kv-cache-dtype fp8 \ --enable-prefix-caching \ --speculative_config.num_speculative_tokens 3 \ --mamba-backend flashinfer \ --mamba-cache-mode align \ --reasoning-parser nemotron_v3 \ --speculative_config.model $DSPARK_CKPT \ --tool-call-parser qwen3_coder \ --enable-auto-tool-choice For more details on how to deploy and use the model — see the 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 Nemotron-3.5-Lightning-30B-A3B-NVFP4 General Knowledge MMLU Pro 81.94 81.62 AA-Omniscience 17.50 16.63 Reasoning GPQA Diamond (no tools) 75.44 75.57 HLE (text-only, no tools) 11.72 10.47 SciCode 32.60 31.38 Coding & Agentic SWE-bench Verified 51.56 52.80 SWE-bench Multilingual 39.33 36.47 Terminal-Bench 2.1 24.58 23.46 PinchBench 85.37 83.43 BrowseComp 36.97 36.81 τ³-bench (Banking) 9.28 9.48 GDPval-AA-V2 832 865 Instruction Following IFBench (loose) 71.88 72.88 Long Context AA-LCR 52.00 49.19 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. These numbers were measured with and apply to the official NVFP4 checkpoint 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 NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 is a general purpose reasoning and chat model intended to be used in English and coding languages. Other non-English languages (Spanish, French, German, Italian, Japanese) are also supported. Intended for developers designing AI Agent systems, chatbots, RAG systems, and other AI-powered applications. Also suitable for typical instruction-following tasks. Release Date Hugging Face — 08/11/2026 Model Architecture Architecture Type: Mixture-of-Experts Hybrid (Mamba + Transformer) Network Architecture: Nemotron-3-Lightning + Multi-Token Prediction (MTP) Number of model parameters: 30B Total / 3B Active Model Design The model was pre-trained with over 20T tokens and supports up to 1M context length. The pre-training phase used an NVFP4 recipe. The model includes Multi-Token Prediction (MTP) layers, which predict multiple future tokens to provide richer training signals. Training Methodology Stage 1: Pre-Training NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 model was pre-trained using an NVFP4 recipe with crawled and synthetic code, math, science, and general knowledge data. Software used for pre-training: Megatron-LM Stage 2: Continued Pre-Training for Multi-Token Prediction (MTP) The model underwent a continued pre-training phase to train its Multi-Token Prediction (MTP) layers. In this stage, MTP heads learn to predict multiple future tokens, providing richer training signals to the base model. This phase aligns the MTP layers with the base model's distribution. Stage 3: Supervised Fine-Tuning The model was further fine-tuned on synthetic code, math, science, tool calling, instruction following, structured outputs, and general knowledge data. This stage incorporated data designed to support long-range retrieval and multi-document aggregation. Stage 4: Reinforcement Learning The model underwent multi-environment reinforcement learning using GRPO (Group Relative Policy Optimization) across math, code, science, instruction following, multi-step tool use, multi-turn conversations, and structured output environments. It utilized an asynchronous RL architecture that decouples training from inference and leverages MTP to accelerate rollout generation. Software used for reinforcement learning: NeMo RL, NeMo Gym Stage 5: Post-training Quantization (PTQ) We performed post-training quantization (PTQ) with Nvidia Model Optimizer using the following recipe: Four Over Six NVFP4 (a variant of static MSE calibration) W4A16 on routed and shared experts, FP8 per-tensor dynamic scales on mamba in_proj/out_proj and KV cache. We used a subset of the Nemotron Ultra validation set for calibration with 1000 samples at 32k token length. NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 is a result of the above work. Input Input Type(s): Text Input Format(s): String Input Parameters: One-Dimensional (1D): Sequences Other Properties Related to Input: Maximum context length up to 1M tokens. Supported languages include English, Spanish, French, German, Italian, and Japanese. Output Output Type(s): Text Output Format: String Output Parameters: One-Dimensional (1D): Sequences Other Properties Related to Output: Maximum context length up to 1M tokens Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. Software Integration Runtime Engine(s): PyTorch Supported Hardware Microarchitecture Compatibility: NVIDIA Blackwell; NVIDIA Hopper (NVFP4 / W4A16); NVIDIA Ampere (W4A16) Preferred/Supported Operating System(s): Linux The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment. Model Version(s) GA (08/11/2026) Quick Start Guide All deployment snippets below assume: export MODEL_CKPT=nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 And for DSpark: export DSPARK_CKPT=nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4-DSpark Speculative Decoding Strategies Lightning 3.5 ships with two external draft models for speculative decoding as well as MTP (Multi-Token Prediction). While we currently recommend DSpark for all cases - your usecase may align with DFlash and MTP: DSpark: A semi-autoregressive speculative-decoding drafter that proposes a whole block of candidate tokens in a single forward pass from a parallel backbone. This is recommended for DGX Spark, as well as low-concurrency data centre deployments. DFlash: A speculative-decoding drafter that uses a lightweight block-diffusion model to generate an entire draft block in one forward pass. MTP: A modeling technique that trains the network to predict several future tokens at each position instead of only the next one. vLLM For more indepth instructions on how to deploy through vLLM, head here vLLM Nightly: vllm/vllm-openai:v0.27.1 1x DGX Spark (GB10) Specdec method - DSpark: vllm serve --model $MODEL_CKPT \ --moe-backend marlin \ --kv-cache-dtype fp8 \ --max-model-len 1048576 \ --enable-prefix-caching \ --speculative_config.num_speculative_tokens 3 \ --mamba-backend flashinfer \ --mamba-cache-mode align \ --reasoning-parser nemotron_v3 \ --speculative_config.method dspark \ --tool-call-parser qwen3_coder \ --enable-auto-tool-choice 1x H100 For max throughput