Learn how to deploy Meta’s powerful open-source Llama 3.1 405B model using Ollama on RunPod. With benchmark-crushing performance, this guide walks you through setup and deployment.

Learn how to optimize your serverless GPU deployment on Runpod to balance latency, performance, and cost. From active and flex workers to Flashboot and scaling strategy, this guide helps you build an efficient AI backend that won’t break the bank.

Learn when to use open source vs. closed source LLMs, and how to deploy models like Llama-7B with vLLM on Runpod Serverless for high-throughput, cost-efficient inference.

Runpod has reduced prices by up to 40% across Serverless and Secure Cloud GPUs—making high-performance AI compute more accessible for developers, startups, and enterprise teams.

RAG and fine-tuning are two powerful strategies for adapting large language models (LLMs) to domain-specific tasks. This post compares their use cases, performance, and introduces RAFT—an integrated approach that combines the best of both methods for more accurate and adaptable AI models.

Retrieval-Augmented Generation (RAG) and fine-tuning are powerful ways to adapt large language models. Learn the key differences, trade-offs, and when to use each.

Explore how to deploy and benchmark LLMs locally using tools like Ollama and NVIDIA NIMs. This deep dive covers performance, cost, and scaling insights across GPUs including RTX 4090 and H100 NVL.