the basic rules i follow when building an AI server at home >direct lanes, x16 or x8, from CPU and never off chipset
>no risers unless absolutely necessary
>airflow must be front-to-back, no hot recirculation
>power budget for transient spikes, not just average draw
>always
@theahmadosman
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Home AI Server Building Best Practices Guide
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RTX 5090 vs 4x 3090s VRAM comparison for LLM inference
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5090 has 32GB of VRAM, 4x 3090s have 96GB of VRAM when it comes to LLMs inference, we care more about memory as models are better fully offloaded into VRAM than being shared across system RAM and a single RTX 5090's VRAM
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Economic Insecurity in the Age of AI Acceleration
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Nobody is safe in this economy A C C E L E R A T E
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Ollama’s bloated wrapper fails to match ggml’s efficiency
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do not use Ollama ggerganov wrote blazing-fast
C++ inference (ggml, llama.cpp) then Ollama wrapped it
in a bloated binary and is now somehow the face of local LLMs
soaking up VC hype and it's not even a good wrapper lol -
Run Claude Code Locally on Your Own GPU Setup
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tired of Anthropic’s weekly limits and nerfed models? with one command and a few GPUs,
you can route Claude Code to your own local LLM Buy a GPU p.s. full video tutorial pinned at the top of my profile -
ChatGPT-5 Pro Generates Custom Dev Tool Configurations
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pro tip: tell ChatGPT-5 Pro (or o3) “build me tmux/neovim/vscode configs based on my workflow from our conversations” you’ll be surprised how good it actually is
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Ollama Alternatives: LMStudio, Llama.cpp, vLLM and More
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ollama alternatives > lmstudio
> llama.cpp
> exllamav2/v3
> vllm
> sglang among many others like literally anything is better than ollama lmao -
Build Your Own Byte-Pair Encoder: LLM Engineering Fundamentals
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step-by-step LLM Engineering Projects each project = one concept learned the hard (i.e. real) way Tokenization & Embeddings > build byte-pair encoder + train your own subword vocab
> write a “token visualizer” to map words/chunks to IDs
> one-hot vs learned-embedding: plot -
Monitor r/buildapcsales for AI hardware deals and computing equipment
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keep an eye on r/buildapcsales