Runpod Blog

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.

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.

Run MoonshotAI’s Kimi-K2-Instruct on RunPod Instant Clusters using H200 SXM GPUs and a 2TB shared network volume for seamless multi-node training. This guide shows how to deploy with PyTorch templates, optimize Docker environments, and accelerate LLM inference with scalable, low-latency infrastructure.

As prompt complexity increases, large language models (LLMs) hit a “cognitive wall,” suffering up to 40% performance drops due to task interference and overload. By decomposing workflows into iterative refinement chains (e.g., the Self-Refine framework) and deploying each stage on serverless platforms like RunPod, you can maintain high accuracy, scalability, and cost efficiency.

Runpod enhanced its referral program with exciting new features including randomized rewards up to $500, a premium affiliate tier offering 10% cash commissions, and continued lifetime earnings for existing users, creating more ways than ever to earn while building the future of AI infrastructure.

Moonshot AI’s Kimi-K2-Instruct is a trillion-parameter, mixture-of-experts open-source LLM optimized for autonomous agentic tasks—with 32 billion active parameters, Muon-trained performance rivaling proprietary models (89.5 % MMLU, 97.4 % MATH-500, 65.8 % pass@1), and the ability to run inference on as little as 1 TB of VRAM using 8-bit quantization.

RunPod’s new S3-compatible API lets you manage files on your network volumes without launching a Pod. With support for standard tools like the AWS CLI and Boto3, you can upload, sync, and automate data flows directly from your terminal — simplifying storage operations and saving on compute costs. Whether you’re prepping datasets or archiving model outputs, this update makes your AI workflows faster, cleaner, and more flexible.