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Orchestrating GPU workloads on Runpod with dstack
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.
AI Workloads

DeepSeek V3.1: A Technical Analysis of Key Changes from V3-0324
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.
AI Workloads

From No-Code to Pro: Optimizing Mistral-7B on Runpod for Power Users
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.
Learn AI

Wan 2.2 Releases With a Plethora Of New Features
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.
AI Infrastructure

Deep Cogito Releases Suite of LLMs Trained with Iterative Policy Improvement
Deploy DeepCogito’s Cogito v2 models on Runpod to experience frontier-level reasoning at lower inference costs—choose from 70B to 671B parameter variants and leverage Runpod’s optimized templates and Instant Clusters for scalable, efficient AI deployment.
AI Infrastructure

Comparing the 5090 to the 4090 and B200: How Does It Stack Up?
Benchmark Qwen2.5-Coder-7B-Instruct across NVIDIA’s B200, RTX 5090, and 4090 to identify optimal GPUs for LLM inference—compare token throughput, cost per token, and memory efficiency to match your workload with the right performance tier.
Hardware & Trends