DROP EVERYTHING The bible for running LLMs locally is now available online to read for free Covers what to use on – Laptop / edge / odd hardware
– Mac-first workflows
– Single RTX GPUs
– 2-4+ NVIDIA / CUDA GPUs
– General production serving
– Long-context / MoE / routing
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@theahmadosman
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Free online bible for running LLMs locally on any hardware
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Understanding Inference Engines for Local AI Model Deployment
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Inference Engines and why they matter when you're running models locally High-level overview article that, in my honest opinion, should be read by everyone in tech Let me know if you have questions / requests for next one
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A Guide to Setting Up Local AI Environments
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Gentle reminder that all you need to start with Local AI is: – 2x RTX 3090s (pick up for $700-$900 on r/hardwareswap) – Qwen 3.6 27B / Gemma 4 31B – Your favorite agent (Claude Code / OpenCode / etc) – Self-hosted SearXNG for web access And you got yourself Opus 4.5 at home
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Local LLM execution and cognitive security benefits
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I have ran LLMs locally that had 2048 / 4096 / 8192 context windows That alone keeps me from ever getting one-shot by an AI into a psychosis Being able to tinker with these things is legit good for you CogSec, just saying
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GPU Memory Math for LLMs Explained
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First, GPU Memory Math for LLMs (or why it is not always about Memory Size)
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Strategic value of open-source AI models and compute costs
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But that’s the thing though, my whole hypothesis was that opensource models are gonna be amazing Waiting until they already are is just reactionary and makes you pay for compute at a steep price
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Technical Discussion on Model Weighting and User Distribution
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Are you updating the weights for your experiences and focuses vs the normal distribution of all the users a model is being served to
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The Future of Continual Learning and Local AI
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Continual Learning has already been solved, it just requires the model weights to be running locally on your own hardware so big labs are avoiding the topic altogether Local / Opensource AI will win Inevitable
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Critique of local AI software stacks and heterogeneous hardware deployment
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Demos in the local AI space between heterogenous devices are misleading -Stuff like mixing MacBooks / Mac Studios with GPUs / DGX Sparks I haven’t seen a single mature software stack in that space yet To talk about it with high certainty is quite dishonest IMHO My 2 cents
