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Local LLMs

2026年本地运行AI模型的完整指南。

本地LLM提供完整隐私、零API成本和离线能力。这些指南涵盖从首次安装到70B模型微调、硬件选择和企业部署的一切内容。

PromptQuorum连接到您的本地LLM(Ollama、LM Studio、Jan AI),并同时将提示词发送给25+个云模型——在一个视图中比较本地与云端结果。

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Getting Started

入门:如何运行您的第一个本地LLM?

Zero-to-running in under 10 minutes. OS-specific installation guides, first-model walkthroughs, and a privacy-first setup checklist for beginners.

Models by Use Case

按用例分类的模型:您实际上应该使用哪个本地LLM?

Model reviews, benchmark comparisons, use-case winners, and quantization guides for Llama 4, Qwen3.5, DeepSeek, Gemma 4, and 70B+ models.

Tools & Interfaces

工具与界面:哪款软件让您启动最快?

Software showdowns, GUI comparisons, API setups, and front-end guides — Ollama, LM Studio, OpenWebUI, vLLM, llama.cpp, and more.

Hardware & Performance

硬件与性能:运行本地LLM您真正需要什么?

Real hardware recommendations, VRAM math, GPU benchmarks, quantization trade-offs, and optimization tricks for RTX 5090, 4090, Mac Silicon, and budget builds.

Advanced Techniques & Applications

高级技术:如何超越基本聊天?

Beyond basic chat — local RAG pipelines, LoRA fine-tuning, LangGraph agents, coding workflows, multimodal models, and custom model creation.

Enterprise

企业:组织如何大规模部署本地LLM?

On-prem deployment, air-gapped setups, GDPR/HIPAA compliance, multi-user scaling, and private RAG for organizations requiring full data sovereignty.

GPU Buying Guides

GPU购买指南:您应该为本地LLM购买哪个GPU?

GPU recommendations, budget picks, next-gen comparisons, and used-market value for running 7B to 70B models.

Hardware Setups

硬件设置:为本地LLM您需要什么计算机?

Complete system builds, mini PCs, laptops, and workstations at multiple price points for serious local inference.

Privacy & Business

隐私与业务:您如何为组织保护本地LLM?

Secure on-premises setups, multi-user deployments, NAS storage, and offline workflows for compliance-heavy organizations.

Cost & Comparisons

成本与比较:本地vs云vs订阅—哪个更便宜?

ROI analysis, price comparisons, total cost of ownership, and platform comparisons (Local vs CloudGPU vs Subscriptions).

Local LLMs 2026: Complete Guide to Running AI Models Offline | PromptQuorum