Got it, thanks for clarifying. To summarize:

Running moderately sized language models (millions of parameters) on a medium-end laptop is feasible today, but response times would likely be on the order of seconds, not real-time. The exact latency would depend on various factors, but sub-second response is unlikely with current hardware and algorithms.

As technology improves over time, response times could decrease and larger models could become practical on consumer laptops, but we are still quite far from running models equivalent to GPT-3, if ever. Significant hardware and software advances would be required to reach that scale.

If real-time response is a priority, it may be better to focus on innovations to make smaller models more capable, such as through training, pruning or other techniques. More progress is needed, but that could be a more promising path than just scaling up model size given current limitations.

Does that help summarize the key points regarding running language models on medium-end laptops? Let me know if you would like me to elaborate on any of the areas or address other questions.
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