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Source channel @githubtrending · Post #15263 · Nov 2

#python#deep_learning#inference#llm#nlp#pytorch#transformer Nano-vLLM is a small, fast, and easy-to-understand tool for running large language models offline. It matches the speed of bigger systems like vLLM but uses only about 1,200 lines of clean Python code, making it simple to read and modify. It includes smart features like prefix caching and tensor parallelism to boost performance. You can install it easily and run models like Qwen3-0.6B on your own GPU. This tool is great if you want fast, efficient AI inference without complex setups, ideal for learning, research, or small deployments on limited hardware. https://github.com/GeeeekExplorer/nano-vllm

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The 2ndDim: That was I talking about!

@The2ndDim · Post #1506 · 07/28/2021, 08:18 PM

#WantThis#SBC#RaspberryPi https://www.youtube.com/watch?v=huAKEbyPcBc https://forum.radxa.com/t/introduce-the-radxa-zero/6550 有点厉害 性能吊打树莓派ZeroW 可惜视频只走MicroHDMI CPU: AmLogic S905Y2 (4核心 Cortex-A53 1.8 GHz, 12nm制程) GPU: Mali G31 MP2 RAM: LPDDR4 512MB/1GB/2GB/4GB 存储: 板载eMMC 5.1 8/16/32/64/128GB + MicroSD扩展 HDMI: Micro HDMI, HDMI 2.1, 4K@60 HDR 硬件解码: H265/VP9 decode 4Kx2K@60 无线: WiFi4/BT4 或 WiFi5/BT5 USB: 1x USB 2.0 Type C OTG (数据+供电), 1x USB 3.0 Type C host GPIO: 40Pin GPIO, ADC/UART/SPI/PWM 其他: Crypto Engine, 支持外置天线, 一个硬件按钮 系统: 目前有 Android 9, TwisterOS (基于Armbian的Ubuntu 20.04分支), Manjaro, EmuELEC