Honestly, skip the standard VPS route if you dont want to lose your mind trying to get CUDA drivers working. RunPod is honestly a game changer for this stuff! It is super easy to just spin up a pod with an NVIDIA RTX 3090 24GB VRAM and test things out without committing to a massive contract. For your budget, you can definitely afford some solid hourly compute there. DigitalOcean is great for hosting static sites, but trying to squeeze model inference out of a regular Droplet is just a recipe for frustration tbh. Just grab a template on RunPod, load your model, and you will have your chatbot up and running way before October. Its so much better than fighting with linux configs for days. Youre gonna love it!

Honestly, if you are planning to run your own models, keep in mind that raw performance is only half the battle. I spent months trying to get a decent inference pipeline running on standard infrastructure, and it was a complete mess with memory swaps and cooling throttling. You really need dedicated VRAM if you want anything close to sub-second responses for your chatbot. Before you dive into a long-term commitment, I would suggest looking at Vast.ai RTX 3090 24GB VRAM instances. They allow you to rent compute by the hour, which is perfect for your October deadline because you arent locked into a monthly bill if you find a more efficient model configuration. Be careful about your model size too. If you go for a massive parameter count, you will eat through your budget instantly. I recommend sticking to something manageable like a quantized Llama 3 or Mistral that fits comfortably within 16GB of VRAM. Using NVIDIA A6000 48GB VRAM might seem like overkill now, but if you have high traffic, those extra cycles save you from timeouts. Just make sure to containerize your code so you can jump ship to another provider if your current host starts lagging or raising prices. Managing your own dependencies like CUDA versions is a nightmare, so use a pre-built image to save yourself the stress.

TL;DR: If you need to keep costs under 50 bucks and dont want a dev headache, stick to serverless inference or rented GPU pods. I tried running a small model on a standard DigitalOcean Droplet Basic 8GB RAM 4 vCPUs a while back and honestly, it was painful. Loading times for the model weights were slow and it hit memory limits the second someone asked a follow-up question. Since you are already in Seattle, latency to major data centers is fine, but the real issue is just getting the VRAM you need. If you go with RunPod NVIDIA RTX 3090 24GB VRAM, you can just pull a pre-built docker container and be live in minutes. Its basically plug and play compared to fighting with Linux dependencies on a generic VPS. Just dont leave your instance running 24/7 if you arent getting traffic.