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詳細を見る ↗**NVIDIA Nemotron 3.5 Bolt 74B-A9B 24GB** is a vision-language model (VLM) designed to run everyday agentic tasks locally, using a native 2-bit quantization to fit in 24GB of memory and run alongside other applications. It optimizes intelligence and speed per GB. It aims to achieve the optimal balance between speed and intelligence, being faster than most popular dense models while smarter than most popular MoE models with a similar memory footprint. 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. For more details on how to deploy and use the model — see the Quick Start Guide below!
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詳細を見る ↗NVIDIA-Nemotron-3.5-Bolt-74B-A9B-24GB-LiLiCorr is the LiLiCorr speculative-decoding checkpoint for the NVIDIA-Nemotron-3.5-Bolt-74B-A9B-24GB. The compatible target is a hybrid LatentMoE vision-language model for reasoning, chat, and agent workflows. For more information, see the NVIDIA-Nemotron-3.5-Bolt-74B-A9B-24GB target card. This checkpoint supports LiLiCorr-assisted serving of the compatible NVIDIA-Nemotron-3.5-Bolt-74B-A9B-24GB target. This checkpoint is not a standalone language model. It proposes draft token candidates that the compatible 24GB target verifies; the target produces the user-visible text.
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詳細を見る ↗NVIDIA-Nemotron-3.5-Bolt-74B-A9B-24GB-LiLiCorr-GGUF is the LiLiCorr speculative-decoding checkpoint for NVIDIA-Nemotron-3.5-Bolt-74B-A9B-24GB-GGUF. The compatible target is a hybrid LatentMoE vision-language model for reasoning, chat, and agent workflows. For more information, see the NVIDIA-Nemotron-3.5-Bolt-74B-A9B-24GB-GGUF target card. This checkpoint is not a standalone language model. It proposes draft token candidates that the compatible 24GB target verifies; the target produces the user-visible text. **GOVERNING DOWNLOAD TERMS:** Use of this model is governed by the OpenMDW-1.1 model license.
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詳細を見る ↗NVIDIA Nemotron 3.5 Bolt 74B-A9B is a vision-language model (VLM) designed to run everyday agentic tasks locally. This is the full-precision (BF16) release of this model, intended primarily for **customization and post-training** rather than direct production inference. The 24GB version of this model uses a native 2-bit quantization to fit in 24GB of memory and run alongside other applications. It aims to achieve the optimal balance between speed and intelligence, being faster than most popular dense models while smarter than most popular MoE models with a similar memory footprint. For latency- and throughput-optimized inference, use this 24GB version for llama.cpp or vLLM instead. 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.
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詳細を見る ↗**NVIDIA-Nemotron-3.5-Bolt-E4B-4GB** is a small language model (SLM) further post-trained from Gemma-4-E4B-it by NVIDIA. Nemotron-3.5-Bolt-E4B carries forward Gemma’s strong conversational capabilities and further improves multi-turn tool use, instruction following, and input grounding for fast local voice/device assistants, interactive personas, and embedded/edge interfaces. Designed for low-memory, low-TTFT operation, the model allows agents to respond quickly while preserving headroom for applications or adjacent model pipelines. For speculative decoding, use the companion DFlash drafter. For the full-precision (BF16) version, see NVIDIA-Nemotron-3.5-Bolt-E4B-BF16. 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.
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詳細を見る ↗The NVIDIA-Nemotron-3.5-Bolt-E4B-4GB-DFlash model is the GGUF (a binary model file format used by llama.cpp) version of NVIDIA-Nemotron-3.5-Bolt-E4B-BF16-DFlash, the DFlash speculative decoding draft checkpoint for NVIDIA-Nemotron-3.5-Bolt-E4B, a reasoning language model post-trained by NVIDIA from Google's Gemma 4 E4B IT. It is converted to GGUF in BF16 (bfloat16) for use with llama.cpp and must be loaded together with the GGUF version of the target model, Nemotron-3.5-Bolt-E4B-4GB. The draft model does not include token embeddings or an output head, and uses those of the target model at runtime. The model is intended for lower-latency local serving of NVIDIA-Nemotron-3.5-Bolt-E4B on workstation GPUs. Developers running NVIDIA-Nemotron-3.5-Bolt-E4B locally with llama.cpp for reasoning, chat, tool use, and agentic workflows that benefit from lower-latency speculative decoding on workstation GPUs. This release is intended for DFlash-assisted serving together with NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF rather than as a standalone model checkpoint. **Architecture Type:** Transformer **Network Architecture:** Other - DFlash draft model (five Qwen3-style decoder layers) **Number of Model Parameters:** 360M (360,480,000), stored in BF16 in a single GGUF file of 736,812,096 bytes (736.81 MB / 702.68 MiB). The draft model shares the token embeddings and output head of the target model, so they are not included in this checkpoint.
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詳細を見る ↗**NVIDIA-Nemotron-3.5-Bolt-E4B-BF16** is a small language model (SLM) further post-trained from Gemma-4-E4B-it by NVIDIA. This is the full-precision (BF16) release. Nemotron-3.5-Bolt-E4B carries forward Gemma’s strong conversational capabilities and further improves multi-turn tool use, instruction following, and input grounding for fast local voice/device assistants, interactive personas, and embedded/edge interfaces. Designed for low-memory, low-TTFT operation, the model allows agents to respond quickly while preserving headroom for applications or adjacent model pipelines. For speculative decoding, use the companion DFlash drafter. For the Q4_K_M quantized version, see NVIDIA-Nemotron-3.5-Bolt-E4B-4GB. 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.
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詳細を見る ↗> *Looking for the post-trained model? See NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 for the full-precision release, or NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 for optimized inference.* **NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16** is a large language model (LLM) trained by NVIDIA. This is the base (pre-trained) checkpoint of the Nemotron 3.5 Lightning family — no supervised fine-tuning, reinforcement learning, or distillation has been applied — making it the natural starting point for developers and researchers building their own post-trained models. The model employs a hybrid **Mixture-of-Experts (MoE)** architecture, utilizing interleaved Mamba-2 and MoE layers, along with select Attention layers. The model incorporates **Multi-Token Prediction (MTP)** layers — trained via a dedicated continued pre-training phase — for richer training signals and native speculative decoding, and it is pre-trained using an **NVFP4** recipe to maximize compute efficiency. The model has **3B active parameters** and **30B parameters in total**.
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詳細を見る ↗* The pre-training data has a cutoff date of September 2025. * The post-training data has a cutoff date of May 2026. 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. **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.
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詳細を見る ↗* The pre-training data has a cutoff date of September 2025. * The post-training data has a cutoff date of May 2026. 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. **NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4** is a large language model (LLM) trained by NVIDIA.
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詳細を見る ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗The NVIDIA Nemotron-3.5-Lightning-30B-A3B-NVFP4-DSpark model is the DSpark speculative decoding checkpoint for NVIDIA's Nemotron-3.5-Lightning-30B-A3B model family, which is a hybrid LatentMoE language model designed for reasoning, chat, and agentic workflows. For more information, please check BF16, NVFP4. The NVIDIA Nemotron-3.5-Lightning-30B-A3B-DSpark-NVFP4 model is intended for DSpark speculative decoding deployments tuned for DGX Spark and low-concurrency data centre workflows. **GOVERNING DOWNLOAD TERMS:** Use of this model is governed by the OpenMDW-1.1 model license. Developers deploying Nemotron-3.5-Lightning-30B-A3B for reasoning, chat, RAG, and agentic workflows that benefit from lower-latency speculative decoding on DGX Spark and data centre GPUs. This release is intended for DSpark-assisted serving of Nemotron-3.5-Lightning-30B-A3B rather than as a standalone target model checkpoint.
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詳細を見る ↗**NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4** is the quantized release of NVIDIA Nemotron 3.5 Super VL, a vision-language model for reasoning, coding, tool use, document understanding, and video understanding. It uses NVFP4 quantization to reduce memory requirements, with quantization-aware distillation from the BF16 model. The model combines interleaved Mamba-2, Mixture-of-Experts (MoE), and attention layers with a C-RADIO vision encoder and a vision-language connector. It has **120B total parameters** and **12B active parameters** and supports context lengths up to **1M tokens**. Multi-Token Prediction (MTP) enables native speculative decoding. 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.
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詳細を見る ↗The NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4-LiLiCorr model is the LiLiCorr speculative decoding checkpoint for NVIDIA's Nemotron-3.5-Super-VL-120B-A12B model family, which is a hybrid LatentMoE vision-language model designed for reasoning, chat, visual understanding, and agentic workflows. For details about the target model, see the BF16 and NVFP4 model cards. The NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4-LiLiCorr model is intended for LiLiCorr speculative decoding deployments tuned for DGX Spark and low-concurrency data center workflows. This model was built through our ModelOpt library which is a library of model optimization techniques including quantization, pruning, Neural Architecture Search (NAS), distillation, speculative decoding and sparsity to accelerate models. Developers deploying Nemotron-3.5-Super-VL-120B-A12B for reasoning, chat, RAG, and agentic workflows that benefit from lower-latency speculative decoding on DGX Spark and data center GPUs. Load this draft checkpoint alongside the Nemotron-3.5-Super-VL-120B-A12B target model to enable LiLiCorr-assisted serving.
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詳細を見る ↗NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16 is a base large language model (LLM) trained from scratch by NVIDIA, with the next token prediction loss. It provides a good starting point for instruction fine-tuning. 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. GOVERNING TERMS: Use of this model is governed by the NVIDIA Nemotron Open Model License Agreement.
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詳細を見る ↗* The post-training data has a cutoff date of November 28, 2025\. * The pre-training data has a cutoff date of June 25, 2025\. Nemotron-3-Nano-30B-A3B-BF16 is a large language model (LLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. The model's reasoning capabilities can be configured through a flag in the chat template. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be configured to do so, albeit with a slight decrease in accuracy for harder prompts that require reasoning. Conversely, allowing the model to generate reasoning traces first generally results in higher-quality final solutions to queries and tasks. The model employs a hybrid Mixture-of-Experts (MoE) architecture, consisting of 23 Mamba-2 and MoE layers, along with 6 Attention layers. Each MoE layer includes 128 experts plus 1 shared expert, with 6 experts activated per token. The model has 3.5B active parameters and 30B parameters in total.
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