RunPod Blog.

Runpod has officially achieved SOC 2 Type II certification, validating that its enterprise-grade security controls not only meet strict design standards but also operate effectively over time. This milestone proves Runpod’s ongoing commitment to protecting customer data and maintaining trusted, compliant AI infrastructure for enterprises and developers alike.

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.

Slurm on RunPod Instant 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.

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 Instant Clusters.

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.