this post was submitted on 13 Nov 2023
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LocalLLaMA
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Community to discuss about Llama, the family of large language models created by Meta AI.
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It doesn't really make that much sense at runtime. By the time you get to running large enough models (think GPT-4) you will already have infrastructure built up from training, which you can then use for inference. Why not run queries through that 1 data center, to minimize latency? For pooled computing resources (prompts are run through 1 member in a pool, kinda like sheepit render farm) it would make more sense, but you're still limited by varying user hardware and software availability. People might have 1060s or 4090s, mistral 7Bs or llama-70Bs. Providing a service to end users means either (1) forcing users to accept quality inconsistency, or (2) forcing providers to maintain very specific software and hardware, plus limiting users to few models.