GPU and AI Guides

Run Wan 2.2 and Wan 2.2 Animate in ComfyUI on a Runpod pod, size the GPU and network volume, use the Lightning LoRA, and serve the workflow through Public Endpoints or Serverless.

Measured Wan 2.2 and LTX-2.3 benchmarks across 23 GPUs, covering seconds per clip, peak VRAM and cost per clip, plus the memory cliff below 80 GB.

Size a GPU cluster from parameters and training method, then compare orchestration, interconnect, billing and commitment across Runpod, CoreWeave, Lambda, Nebius, AWS, Google Cloud and Azure.

Measured vLLM benchmarks across 21 GPUs and 15 models, covering tokens per second per user, time to first token and cost per million output tokens.

Measured Whisper benchmarks across 23 GPUs and four variants, covering realtime factor, peak VRAM and cost per hour of audio.

Measured ComfyUI results across 14 GPUs on SDXL, FLUX.2 and Qwen-Image, alone and under shared load. What VRAM you need, and what sharing a pod really costs.

How to stress test a GPU, which tools to use, and how to tell a healthy card from a failing one before you trust a long training run to it.

Slurm is the open-source job scheduler used by most of the world's top supercomputers. What it does, the commands you need, and when to use it.

A neocloud is a cloud provider built specifically for GPU compute. What the term means, how neoclouds differ from hyperscalers, and when to use one.

Autonomous AI agents fail differently than chatbots. Runaway loops, silent tool errors, and traffic bursts can quietly rack up GPU costs. Learn how proper LLM tool calling design, step limits, and autoscaling keep open-weight agents reliable and affordable on Runpod Serverless.

Speculative decoding explained: how draft-and-verify cuts LLM latency, when it helps, and the vLLM settings to enable it on Runpod Serverless.

gpt-oss-120b is OpenAI's open-weight reasoning model. Call it on a hosted endpoint, or self-host it with vLLM on a single 80 GB GPU.