The Runpod Blog

Runpod Serverless now scores fallback GPU types so workers can land on compatible capacity when your first choice is contended.

Optimize multi-node GPU cluster performance and cost-efficiency by aligning parallelism strategies, network fabric selection, and performance benchmarking.

Qwen 3.8 is punching well above its weight for its relatively small size compared to big foundational models. Is there a such thing as a free lunch?

Our pricing philosophy in one line: we move prices to keep GPUs available.

Deploy Qwen3.8-27B on Runpod Serverless with vLLM, then send your first request with curl and the OpenAI Python SDK.

vLLM cold start optimization on Runpod Serverless: compile cache, weight prefetch, CUDA graph config

Which kind of AI actually solves my problem?