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local-llm
A comprehensive guide for building and configuring a high-end local machine to run state-of-the-art LLMs, with detailed hardware choices, BIOS tuning, and Docker-based model serving.
AI 仓库情报摘要
FR-AI / ANALYSIS为什么值得关注
它颠覆了主流的大语言模型中心 VLA 范式，通过直接的双向视觉-语言交互证明：小模型可以匹配甚至超越更大模型，同时大幅降低计算和内存需求，使实时部署成为可能。
适合谁使用
- 机器人操作研究者
- 具身智能开发者
- 机器人学与机器学习学生
- 希望在消费级 GPU 上部署低延时策略的工程师
典型使用场景
- 在 LIBERO 和 RoboTwin 等仿真基准上进行实时机械臂控制
- 使用一个紧凑策略完成多种操作任务
- 在消费级硬件上进行低成本的 VLA 模型研究与原型开发
- 探索高效的视觉-语言-动作系统架构
项目优势
- 极快的推理速度（31.2 毫秒/步），RTX 4090 上达 32 Hz
- 推理时极低的显存占用（0.9 GB）
- 在 LIBERO 上以仅 0.2B 参数取得 97.7% 的最优成功率
- 开源代码，提供两个基准的详细训练与评估脚本
使用前须知
- 需要使用外部预训练模型（DINOv3、BERT、GroundingDINO）并手动处理数据（去除 no-op）
- 不同基准（LIBERO 与 RoboTwin）需要独立的 Python 环境
- 仅在仿真操作任务上评估，未报告真实机器人实验
README 快速开始
TurboVLA: Real-Time Vision-Language-Action Model at 32 Hz on an RTX 4090 with <1 GB VRAM
Hengyi Xie1*, Chenfei Yao1*, Xianjin Wu1, Xuanyang Xi2, Yiping Tang2, Di Xu2, Yingying Zhu1,
Dingkang Liang1†, Xiang Bai1, Han Ding1
1 Huazhong University of Science and Technology, China 2 Huawei Technologies Co. Ltd, China
- Equal contribution, listed alphabetically by surname. † Project Lead.
This repository contains the official implementation of TurboVLA for the paper TurboVLA: Real-Time Vision-Language-Action Model at 32 Hz on an RTX 4090 with <1 GB VRAM.
📄 Abstract
Vision-language-action (VLA) models commonly adopt an LLM-centric V → L → A pathway, where visual observations are projected into the representation space of a large language model before being decoded into robot actions. Although effective, this design incurs substantial computation and memory overhead at every policy invocation.
In this work, we introduce TurboVLA, a new VLA paradigm that reformulates the conventional V → L → A pathway as a direct V + L → A mapping. Instead of using a large language model as the central interface between perception and action, TurboVLA independently encodes visual observations and language instructions, directly exchanges information between them through lightweight bidirectional vision-language interaction, and predicts continuous action chunks with a compact decoder. This simple design constructs task-conditioned representations directly from visual and linguistic features, significantly reducing the computational and memory costs of VLA inference. On LIBERO, TurboVLA achieves 97.7% average success with only 0.2B parameters, 31.2 ms inference latency, and 0.9 GB inference VRAM on a consumer-grade RTX 4090, matching or outperforming substantially larger VLA policies. These results establish TurboVLA as a simple and effective alternative to the prevailing LLM-centric VLA paradigm, offering a new perspective on how vision, language, and action can be connected for efficient robotic manipulation.
🔍 Overview
📈 Performance
⚙️ Installation
Clone the repository:
git clone https://github.com/H-EmbodVis/TurboVLA.git
cd TurboVLA
LIBERO and RoboTwin use different simulator and data stacks. We recommend separat
项目描述
TurboVLA: Real-Time Vision-Language-Action Model at 32 Hz on an RTX 4090 with <1 GB VRAM
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