How Much VRAM Does an LLM Need? Parameters, Quantization and Context
How much VRAM does an LLM need? Use weights + KV cache + overhead to estimate from parameters, quantization and context, with quick tables for 8GB to 48GB.
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LLM Quantization Explained: What Q4_K_M Means and How to Choose a GGUF Quant
LLM quantization explained: what Q4_K_M means, GGUF quant sizes and speeds side by side, and how to pick Q4, Q5 or Q8 for your VRAM, with Ollama commands.
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Local LLM Appliance Buying Guide: What to Ask Before You Pay (2026)
Local LLM appliance buying guide for 2026: how the market shifted, how mini AI boxes, servers and DIY builds compare, and 10 questions to ask vendors.
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Best GPU for Local LLMs: A VRAM Tier Table (2026)
Find the best GPU for local LLM use: VRAM tiers from 8GB to 32GB, matching models, bandwidth, power draw and dated price ranges, plus buying pitfalls.
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How to Run DeepSeek Locally: Distilled vs Full Model, Picked by VRAM
Run DeepSeek locally the right way: R1 distills vs the full model vs V4, which Ollama tag fits 8GB to 48GB of VRAM, and four commands to get started.
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Best Local LLM in 2026: Pick by VRAM and Task, Not by Leaderboard
Best local LLM for your hardware: picks by 8GB, 12GB, 24GB and 32GB VRAM and by task, covering Qwen, Gemma and gpt-oss, with file sizes and VRAM estimates.
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Run an LLM on 8GB VRAM: What Can an 8GB GPU Run? Model Picks
Run an LLM on 8GB VRAM: Ollama picks for chat, reasoning, coding and vision with file sizes, context settings, 8GB card comparison and upgrade advice.
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Running an LLM on 24GB VRAM: What Can an RTX 3090 / 4090 Run?
Which LLMs fit on 24GB VRAM? 27B–32B dense and 30B MoE models with memory estimates, long-context headroom, 3090 vs 4090, and why 70B doesn’t fit.
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本地大模型是什么？和在线 AI 的区别、适合谁
本地大模型是什么？一文讲清本地、离线、端侧三个词的区别，对比在线 AI 的隐私、成本与能力差异，并说明哪些人适合、需要什么硬件。
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Qwen 本地部署：Qwen3 / 3.5 / 3.6 / 3.8 怎么选尺寸、怎么跑
Qwen 本地部署怎么做？Qwen3、3.5、3.6、3.8、Coder、VL 的 Ollama 文件大小、显存对应表、一键命令、思考模式开关与常见问题。