Brendan McKeag

Use Ollama to compare multiple LLMs side-by-side on a single GPU pod—perfect for fast, realistic model evaluation with shared prompts.

Discover how to boost your LLM inference performance and customize responses using SGLang, an innovative framework for structured LLM workflows.

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.

Runpod Serverless now supports multi-GPU workers, enabling full-precision deployment of large models like Llama-3 70B. With optimized VLLM support, flashboot, and network volumes, it's never been easier to run massive LLMs at scale.

Runpod introduces Serverless CPU: high-performance VM containers with customizable CPU options, ideal for cost-effective and versatile workloads not requiring GPUs.

Deploy any Hugging Face large language model using Runpod’s configurable templates. Customize your endpoint with ease and launch scalable LLM deployments in just a few clicks.

Learn how to set up a Runpod project, launch a Stable Diffusion endpoint, and generate images from text using a simple Python script and the Runpod CLI.