NVIDIA仅拥有发布 Nemotron 3.5 Bolt E4B 4GB，是快速对话agents的一种开放的小语言模型。 它针对在RTX,Jetson和Spark硬件上本地运行的语音助理，游戏NPC和机器人接口。 该型号是从Google的Gemma 4 E4B进行后期训练，只使用强化学习。 对于开发者来说，这是VRAM大约4GB中的agent行为。 这为言论和视觉模型留下了空间。
TL；数据
- 大小：8B总参数，4B有效，上下文64K.
- 运行在：GeForce RTX,Jetson,RTX Spark和DGX Spark. NVIDIA测试中Q4 K M建筑在RTX上使用了3.1至4.3GB VRAM.
- 性能：在21基准测试对阵Gemma 4 E4B,Qwen3.5-4B和Nemotron 3 Nano 4B(NVIDIA-测量)的15分中得分最高。
- 最佳：IHEval 84.8,Gemma 4 E4B和60.6Qwen3.5-4B对阵67.0.
- 最差：Tau2-Bench 55.1,75.1比Qwen3.5-4B落后约20分。
什么是尼莫特龙3.5波尔特E4B?
尼莫特龙 3.5 博尔特E4B是一个来自NVIDIA的小型语言模型，它赋予本地对话权agents. 它将自然对话与多回合工具调用结合起来。 NVIDIA列出了3个目标工作量：设备和语音助理，交互式字符，以及嵌入式或工业性的agents.
研究报告显示 a Reachy Mini机器人运行在Jetson Orin Nano 8GB上。 在该演示中，Cosmos 3 Edge处理视觉，而Bolt处理语音回复和工具呼叫。 NVIDIA小组也打电话博尔特是NVIDIA ACE游戏角色的第三代小模型。
发布文章的核心功能：
- 多回合工具的使用，并附有同步工具调用的指导。
- 一个对话的-模版旗子，用于简短的、准备发言的答复。
- 合理的推理，加上一个时间思考预算来限制延迟。
- 64K语境用于历史，工具定义和应用状态。
Bolt E4B是怎么训练的？
NVIDIA完全跳过监督的微调。 根据研究报导，SFT 改进了调用工具，相反，研究团队通过NeMo-RL和NeMo-Gym使用RLVR和RLHF.
每个培训样本都采用推理和非理性模式。 每批货物都保留了2:1的不合理样本与推理样本的比例。 GENRM环境中的长时间处罚阻止了培训期间的答案增长。 数据集和全食谱公开。
为了部署，NVIDIA将BF16检查站量化为Q4 K M，并进行重要比例校准。 校准使用约1M的RL推出符。 释放还运送了一台用于投机解码的DFlash起草器。 在一台RTX 5070上，带有DFlash的博尔特达到了272.6个令牌/s. Gemma 4 E4B及其MTP的起草器达到了225.9个令牌/s，因此博尔特速度快了1.21x.
尼莫特龙3.5波尔特E4B在基准测试上表现如何？.
NVIDIA将21个基准测试分成5个能力区。 这里所有的分数都是BF16，用NVIDIA在自己的NeMo-Gym吊带中测量。 博尔特在推理模式下带领了21列中的15列。
NVIDIA也报告平均得分大约比Nemotron 3 Nano 4B高40%. 声称在推理驱动的比较中最多减少80%的输出符。 另外，Scale Labs在MultiChallenge上的小型模型中排名博尔特第一位，为56.03%，每个X位。
博尔特不是到处都赢 Qwen3.5-4B领先Tau2-Bench(75.1 vs 55.1)和RULAR 64K(95.1 vs 87.0). 这也导致龙本奇 v2(59.9 vs 50.5)和BFCL-v3(70.9 vs 68.5). Gemma 4 E4B在EQ-Bench v3(41.1 vs 39.4)上保持狭长边缘。
博尔特E4B如何与其他小模型进行比较？
*基准测试列是博尔特发布站点的NVIDIA测量BF16分数。 供应商报告的分数不同。
是什么硬件运行？
NVIDIA在批量尺寸1时用llama.cpp测量了Q4 K M建筑。 下图使用3K输入符和50个输出符。
‘4GB'标签描述了目标内存信封，而不是文件大小。 Hugging Face上的Q4 K M GGUF为6.18GB.
博尔特可以在Hugging Face,Ollama,LM Studio,Unsloth和Jetson AI Lab上找到。 它还作为NVIDIA NIM号以及经由In-Game Inferencing SDK号的舰只。
关键外卖
- Gemma 4 E4B，由NVIDIA进行后期训练，只有RL，没有SFT.
- 在NVIDIA的自带21agent 基准测试中领先15架。
- IHEval 84.8是突出位置；Tau2-Bench 55.1是弱点。
- ‘4GB'是VRAM目标；GGUF文件是6.18GB.
- 开放权重，OpenMDW-1.1下的数据和食谱。
请检查access-date=中的日期值 (帮助) 技术报告，GGUF模型，BF16模型和培训配方。 所有功劳都归功于这个项目的研究人员。 并加入我们的通讯。 等等你在电报里吗现在你也可以加入我们的电报。
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NVIDIA has just released Nemotron 3.5 Bolt E4B 4GB, an open small language model for fast conversational agents. It targets voice assistants, game NPCs and robot interfaces running locally on RTX, Jetson and Spark hardware. The model is post-trained from Google’s Gemma 4 E4B using reinforcement learning only. For developers, this is agent behavior in about 4GB of VRAM. That leaves room for speech and vision models.
TL;DR
- Size: 8B total parameters, 4B effective, 64K context.
- Runs on: GeForce RTX, Jetson, RTX Spark and DGX Spark. The Q4_K_M build used 3.1 to 4.3 GB VRAM on RTX in NVIDIA tests.
- Performance: Top score on 15 of 21 benchmarks against Gemma 4 E4B, Qwen3.5-4B and Nemotron 3 Nano 4B (NVIDIA-measured).
- Best: IHEval 84.8, versus 67.0 for Gemma 4 E4B and 60.6 for Qwen3.5-4B.
- Worst: Tau2-Bench 55.1, about 20 points behind Qwen3.5-4B at 75.1.
What is Nemotron 3.5 Bolt E4B?
Nemotron 3.5 Bolt E4B is a small language model from NVIDIA that powers local conversational agents. It combines natural dialogue with multi-turn tool calling. NVIDIA lists 3 target workloads: device and voice assistants, interactive characters, and embedded or industrial agents.
The research report shows a Reachy Mini robot running on a Jetson Orin Nano 8GB. In that demo, Cosmos 3 Edge handles vision while Bolt handles speech replies and tool calls. NVIDIA team also calls Bolt the third-generation small model for NVIDIA ACE game characters.
Core features from the launch post:
- Multi-turn tool use, with guidance for asynchronous tool calls.
- A conversational_mode flag for short, speech-ready replies.
- Configurable reasoning, plus a runtime thinking budget to cap latency.
- 64K context for history, tool definitions and application state.
How was Bolt E4B trained?
NVIDIA skipped supervised fine-tuning entirely. As per the research report, SFT improved tool calling but hurt Gemma’s conversational quality and verbosity. Instead, the research team used RLVR and RLHF through NeMo-RL and NeMo-Gym.
Every training sample ran in both reasoning and non-reasoning modes. Each batch kept a 2:1 ratio of non-reasoning to reasoning samples. Length penalties in the GenRM environment stopped answers from growing during training. The datasets and the full recipe are public.
For deployment, NVIDIA quantized the BF16 checkpoint to Q4_K_M with importance-matrix calibration. Calibration used about 1M tokens of RL rollouts. The release also ships a DFlash drafter for speculative decoding. On an RTX 5070, Bolt with DFlash reached 272.6 tokens/s. Gemma 4 E4B with its MTP drafter reached 225.9 tokens/s, so Bolt is 1.21x faster.
How does Nemotron 3.5 Bolt E4B perform on benchmarks?
NVIDIA grouped 21 benchmarks into 5 capability areas. All scores here are BF16, measured by NVIDIA in its own NeMo-Gym harness. Bolt led 15 of 21 rows in reasoning mode.
NVIDIA also reports roughly 40% higher average scores than Nemotron 3 Nano 4B. It claims up to 80% fewer output tokens in reasoning-enabled comparisons. Separately, Scale Labs ranked Bolt first among small models on MultiChallenge at 56.03%, per an X post.
Bolt does not win everywhere. Qwen3.5-4B leads on Tau2-Bench (75.1 vs 55.1) and RULER 64K (95.1 vs 87.0). It also leads LongBench v2 (59.9 vs 50.5) and BFCL-v3 (70.9 vs 68.5). Gemma 4 E4B keeps a narrow edge on EQ-Bench v3 (41.1 vs 39.4).
How does Bolt E4B compare with other small models?
*Benchmark rows are NVIDIA-measured BF16 scores from the Bolt launch post. Vendor-reported scores differ.
What hardware does it run on?
NVIDIA measured the Q4_K_M build with llama.cpp at batch size 1. The figures below use 3K input and 50 output tokens.
The ‘4GB’ label describes a target memory envelope, not the file size. The Q4_K_M GGUF on Hugging Face is 6.18 GB.
Bolt is available on Hugging Face, Ollama, LM Studio, Unsloth and Jetson AI Lab. It also ships as an NVIDIA NIM and through the In-Game Inferencing SDK.
Key Takeaways
- Gemma 4 E4B, post-trained by NVIDIA with RL only, no SFT.
- Leads 15 of 21 agent benchmarks in NVIDIA’s own harness.
- IHEval 84.8 is the standout; Tau2-Bench 55.1 is the weak spot.
- ‘4GB’ is the VRAM target; the GGUF file is 6.18 GB.
- Open weights, data and recipes under OpenMDW-1.1.
Check out the technical report, GGUF model, BF16 model and training recipe. All credit goes to the researcher of this project. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
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Asif Razzaq is the CEO of Marktechpost AI Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.
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