This article provides a detailed guide on deploying Llama 3.2 with vLLM and batch processing on a low-cost DigitalOcean Droplet. It demonstrates how to achieve asynchronous inference at significantly lower costs compared to commercial AI APIs like Claude, processing over 10,000 tokens per second for $8/month.
This article demonstrates how to deploy Llama 3.2 with Ollama and WebSocket streaming on a $5/month DigitalOcean Droplet, enabling real-time inference at a fraction of commercial AI API costs. It provides a detailed guide for building a production-ready LLM endpoint that offers significant savings compared to services like Claude or GPT-4.
The content details how to deploy Llama 3.2 1B using TinyLLM and FastAPI on a $5/month DigitalOcean Droplet, achieving sub-100ms latency inference. This setup enables production-grade real-time AI inference, drastically cutting costs and avoiding vendor lock-in.
The content details how to deploy a Llama 3.2 inference cluster using Ollama and Kubernetes on an $8/month DigitalOcean Droplet. This guide aims to provide a cost-effective alternative to commercial AI APIs, enabling production-grade multi-node inference with better latency and zero rate limits.