Newcomer Seeks Recommendations for Local LLM GUIs and Model Selection on Windows
English summary
A new user running local LLMs with an RTX 5090 and 64GB RAM posts on r/LocalLLaMA, overwhelmed by the number of tools and asking for a go-to Windows GUI. They have installed ollama, pulled gemma4 and qwen3.6 models, and seek a comprehensive benchmark resource to compare models like qwen vs gemma. The user is confused by model size variants (e.g., 27B vs 35B) and quantization filenames, wanting to know how to tell if a model fits in VRAM and which to pick for performance.
Chinese summary
一位拥有 RTX 5090 和 64GB 内存的本地 LLM 新手在 r/LocalLLaMA 发帖，因工具繁多而感到不知所措，寻求适用于 Windows 的 GUI 推荐。他已安装 ollama 并下载了 gemma4 和 qwen3.6 模型，请求一份比较 qwen 与 gemma 的综合基准测试资源。用户对模型大小变体（如 27B 与 35B）和量化文件名感到困惑，想知道如何判断模型是否适合显存，以及应选择哪个以获得更好性能。
Key points
User is a local LLM beginner with a high-end setup: RTX 5090, 64GB RAM, Ryzen 9950X3D.
用户是本地 LLM 新手，拥有高端配置：RTX 5090、64GB 内存、锐龙 9950X3D。
They have ollama installed and pulled gemma4 and qwen3.6 models, seeking a Windows GUI recommendation.
他们已安装 ollama 并下载了 gemma4 和 qwen3.6 模型，寻求 Windows GUI 推荐。
They request a comprehensive benchmark comparing model families (qwen vs gemma).
他们请求一份比较模型系列（qwen 与 gemma）的综合基准测试。
They are confused by model size variants and quantization labels, asking how to assess VRAM usage and performance trade-offs.
他们对模型大小变体和量化标签感到困惑，询问如何评估显存占用和性能权衡。