El nuevo modelo abierto de 4 GB de NVIDIA permite ejecutar agentes de voz y personajes de videojuegos de forma rápida y local en hardware de consumo, liberando recursos para otras tareas de IA.
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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 Ne
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