
Cloud vs Local LLMs: Which Setup Fits Your First Project?
The "local is more private, cloud is more powerful" story sounds clean. It's also too simple to make a good decision with.
Read tutorialRunning or assessing language models on user-controlled or locally managed hardware rather than relying only on hosted inference.
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The "local is more private, cloud is more powerful" story sounds clean. It's also too simple to make a good decision with.
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Two people run the same 7B model. One says it needs 4 GB. The other says it needs 9 GB. Neither is lying.
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Two people run the same break-even calculator, feed it the same hardware price, and get opposite answers. Both are right.
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Two model files sit in your download folder. Both say 4-bit. One is smaller, one runs faster, and they give you different answers to the same prompt. If…
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You do not need the best model. You need the model that matches the control you actually require.
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That is the moment this exercise trains you for. You already know how to estimate weight storage, and you have already run one small model and watched its…
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You swap a model to a 4-bit build, run the same prompt, and get a slightly different answer. Now what? You cannot tell whether quantization degraded the…
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The model finishes downloading. You type a prompt. The cursor blinks, the fan spins up, and nothing happens for a while. That pause is the whole question:…
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Running an open source LLM on your own machine today feels closer to installing a desktop app than building a neural network. You don't need a data center,…
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You are comparing models and you see it everywhere: a name, a letter, and a number. Llama 3.1 8B. Mistral 7B. Qwen 72B. The reflex is almost automatic:…
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