Martin Seeger on Nostr: One distinction is essential: training is not inference. During **training**, a model ...

One distinction is essential: training is not inference.

During **training**, a model learns its parameters from enormous datasets through repeated computation across many accelerators. This is where most upfront demand for compute, energy, and training data arises. It is also where many questions about how content was acquired belong.

During **inference**, an already-trained model applies those parameters to a new input. Each run still uses energy, and usage at global scale adds up, but it does not repeat the training process. It is far less resource-intensive per task.

Efficient runtimes and quantization now make surprisingly capable inference possible on home hardware. Qwen3.8-27B is one example.

Training and inference belong to the same system, but not to the same bill. Mixing them together makes the discussion simpler, which is not always the same as making it useful.

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