Runpod Blog.

Slurm on Runpod Clusters makes it simple to scale distributed AI and scientific computing across multiple GPU nodes. With pre-configured setup, advanced job scheduling, and built-in monitoring, users can efficiently manage training, batch processing, and HPC workloads while testing connectivity, CUDA availability, and multi-node PyTorch performance.

Learn how to deploy ComfyUI as a serverless API endpoint on Runpod to run AI image generation workflows at scale. The tutorial covers deploying from Runpod Hub templates or Docker images, integrating with Python for synchronous API calls, and customizing models such as FLUX.1-dev or Stable Diffusion 3. Runpod’s pay-as-you-go Serverless platform provides a simple, cost-efficient way to build, test, and scale ComfyUI for generative AI applications.

dstack is an open-source, GPU-native orchestrator that automates provisioning, scaling, and policies for ML teams—helping cut 3–7× GPU waste while simplifying dev, training, and inference. With Runpod integration, teams can spin up cost-efficient environments and focus on building models, not managing infrastructure.

Learn how to quickly create, test, and deploy Runpod Serverless workers using GitHub templates—accelerating AI workloads with pay-per-use efficiency and zero infrastructure hassle.

DeepSeek V3.1 introduces a breakthrough hybrid reasoning architecture that dynamically toggles between fast inference and deep chain-of-thought logic using token-controlled templates—enhancing performance, flexibility, and hardware efficiency over its predecessor V3-0324. This update positions V3.1 as a powerful foundation for real-world AI applications, with benchmark gains across math, code, and agent tasks, now fully deployable on Runpod Clusters.

Deploy Wan 2.2 on Runpod to unlock next-gen video generation with Mixture-of-Experts architecture, TI2V-5B support, and 83% more training data—run text-to-video and image-to-video models at scale using A100–H200 GPUs and customizable ComfyUI workflows.

Optimize Mistral-7B deployment with Runpod by using quantized GGUF models and vLLM workers—compare GPU performance across pods and serverless endpoints to reduce costs, accelerate inference, and streamline scalable LLM serving.