It's worth pointing out that size isn't everything. From Meta's benchmarking [1] LLaMA 33B outperforms GPT-3 175B, Gopher 280B, Chinchilla 70B and even matches

It's worth pointing out that size isn't everything. From Meta's benchmarking [1] LLaMA 33B outperforms GPT-3 175B, Gopher 280B, Chinchilla 70B and even matches PaLM 540B on a bunch of common evals. Those interested in doing more comparisons can look at https://crfm.stanford.edu/helm/latest/?group=core_scenarios https://crfm.stanford.edu/helm/latest/?group=core_scenarios and https://paperswithcode.com/paper/llama-open-and-efficient-foundation-language-1 https://paperswithcode.com/paper/llama-open-and-efficient-fo... to see where it sits (with some GPT 3.5 and 4 numbers here: https://paperswithcode.com/paper/gpt-4-technical-report-1 https://paperswithcode.com/paper/gpt-4-technical-report-1) I'd agree the secret sauce for how great the newest services perform is probably in the fine-tuning. We're seeing almost daily releases of fine-tuning data sets, training methods and models (at lower and lower costs) so I'm personally pretty optimistic that we'll be seeing some big improvement in self-hosted LLM performance pretty quickly. [1] https://ar5iv.labs.arxiv.org/html/2302.13971#:~:text=Table%203%3A,Sense%20Reasoning%20tasks https://ar5iv.labs.arxiv.org/html/2302.13971#:~:text=Table%2....