Python paperspace Projects
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Judoscale
Save 47% on cloud hosting with autoscaling that just works. Judoscale integrates with Django, FastAPI, Celery, and RQ to make autoscaling easy and reliable. Save big, and say goodbye to request timeouts and backed-up task queues.
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gpt-j
Notebook for running GPT-J/GPT-J-6B – the cost-effective alternative to ChatGPT, GPT-3 & GPT-4 for many NLP tasks. Available on IPUs as a Paperspace notebook. (by graphcore)
Project mention: LLM APIs vs. Self-Hosted Models: Finding the Best Fit for Your Business Needs | dev.to | 2024-12-06Budget: Hosting an LLM can be expensive, and it's important to consider your budget and integration costs. For instance, if you want to host a 6-billion-parameter LLM like GPT-J on a cloud platform such as AWS, you’d likely choose a GPU instance, like the NVIDIA V100 GPU. These instances cost around $3.06 per hour. While this might seem affordable at first glance, it adds up to roughly $26,800 per year for a single instance. If you want to run the model across multiple regions for redundancy, the annual cost can quickly multiply.
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Index
| # | Project | Stars |
|---|---|---|
| 1 | fast-stable-diffusion | 7,708 |
| 2 | gpt-j | 24 |
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