Solution: Reduce Deployment Costs with DeepInfra's Serverless Architecture
For individual developers or SMEs, directly deploying large models such as Llama 3, Mistral, etc. locally usually faces three major pain points: expensive GPU procurement costs, complex operation and maintenance work, and underutilization of resources.DeepInfra provides the following solutions:
- pay-as-you-go model: pay only for the tokens actually used (on average, about $0.5-3 per million tokens), no upfront hardware costs
- Automatic retractability: The platform automatically adjusts computing resources according to the volume of requests, avoiding wastage of resources when they are idle.
- Three Steps to Quick Access: Register account → Get API key → Call through standardized interface, no need to contact server management in the whole process.
Specific implementation can be taken:
1. Prioritize the use of the web version to test the effectiveness of the model
2. Utilize free credits for lesser usage (new users usually have $5-10 trial)
3. The official use of the code is done through themax_tokensParameters control single request consumption
This answer comes from the articleDeepInfra Chat: experiencing and invoking a variety of open source big model chat servicesThe
May not be reproduced without permission:AI productivity tools " How to solve the problem of high hardware costs when deploying open source big models locally?
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