LLM on Raspberry Pi and Jetson: What a Small Board Can Really Run in 2026
Can you run an LLM on Raspberry Pi 5 or a Jetson Orin Nano Super? Bandwidth limits, model sizes, prices and when a small PC or used GPU is the better buy.
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Small Team Shared Local LLM: Sizing, Access Control and Cost for 3 to 10 People
Small team shared local LLM guide: size VRAM for several users, add accounts and access control, compare hardware tiers, and know when to move to vLLM or cloud.
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Windows WSL2 Docker Local LLM Setup: Run Ollama With an NVIDIA GPU in a Container
Windows WSL2 Docker local LLM setup: install WSL 2, share the NVIDIA driver, run Ollama with –gpus, set memory limits, and know when native is simpler.
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RAM vs VRAM for Local LLMs: How CPU Offload Works and What It Costs in Speed
RAM vs VRAM for local LLMs: why memory bandwidth sets speed, how CPU offload splits a model, worked speed ceilings for a 70B model, and when more RAM helps.
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Windows WSL2 Docker 本地大模型部署：在容器里用 NVIDIA 显卡跑 Ollama
Windows WSL2 Docker 本地大模型部署：装 WSL 2、共用 NVIDIA 驱动、用 –gpus 在 Docker 跑 Ollama、设内存上限，并对比原生版。
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AnythingLLM Tutorial: Chat With Your Documents Using Ollama on Your Own Computer
AnythingLLM tutorial: install the desktop app or Docker, connect Ollama, pick an embedder, build workspaces, tune Query mode and fix ‘no relevant information’.
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DeepSeek R1 Distill Sizes Explained: 1.5B, 7B, 8B, 14B, 32B and 70B by VRAM
DeepSeek R1 distill sizes explained: what 1.5B to 70B are, Ollama download sizes, how long reasoning raises memory use, and which size fits 8GB to 48GB.
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Dify and RAGFlow Local Deployment: Build a Private Knowledge Base App With Docker and Ollama
Dify and RAGFlow local deployment: requirements, Docker Compose steps, connecting Ollama, embedding models, and how to choose.
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Home AI Server: How to Build a Private LLM Box for Your NAS and Network
Build a home AI server for local LLMs: hardware tiers, Docker setup with Ollama and Open WebUI, NAS storage, remote access and the security steps that matter.
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LLM Out of Memory Fix: A Local OOM Troubleshooting Checklist (Ollama and llama.cpp)
LLM out of memory fix for local models: check usage first, then cut context and parallelism, quantize the KV cache, and only then change model or hardware.
