Earlier quoted context omitted.
In terms of speed, we're talking about 140t/s for 7B models, and 40t/s for 33B models on a 3090/4090 now.[1] (1 token ~= 0.75 word) It's quite zippy. llama.cpp performs close on Nvidia GPUs now (but they don't have a handy chart) and you can get decent performance on 13B models on M1/M2 Macs. You can take a look at a list of evals here: https://llm-tracker.info/books/evals/page/list-of-evals - for general usage, I th…
Those GPUs are 1200$ and upwards. This is equivalent to 20,000,000 tokens on GPT-4. I don't think I will ever use this many tokens for my personal use.
If people are mostly using their LLMs for specific tasks, then using cloud providers (Vast.ai and Runpod were cheapest last time I checked) can be cheaper than dedicated hardware, especially if your power costs are high. If you're needs are minimal, Google Colab offers a free tier with a GPU w/ 11GB of VRAM, so you can run 3B/7B quantized models easily.
There are reasons of course irrespective of cost to run your own model (offline access, fine-tuning/running task specific models, large context/other capabilities OpenAI doesn't provide (eg, you can run multi-modal open models now), privacy/PII, BCP/not being dependent on a single vendor, some commercial or other non-ToS allowed tasks, etc).
[1] https://gptforwork.com/tools/openai-chatgpt-api-pricing-calc...