Latest AI and tech news
Selecting the right serving engine for your embedding model can dramatically outperform hardware upgrades, yielding up to an 11x throughput increase on the same GPU.
When a private GPU pool beats on-demand GPUs, how reserved capacity is billed, what happens when traffic bursts above the pool, and what usage data to bring before you commit.
Qwen3.8-Flash-Next needs vLLM 0.29, so the Hub's one-click path won't serve it yet. Here are the validated flags, hardware math, and cold-start numbers for running it on Runpod.
Learn how to optimize Model Context Protocol (MCP) tools to prevent oversized responses from exhausting your AI agent's context window.
Learn how to edit videos with Pruna P-Video-Edit using Runpod’s public endpoint, Playground, curl, and Python.
The certification audit closed with zero findings, giving international customers a standing answer instead of a bespoke security questionnaire.
Renting a model means your millionth request costs what your first one did. Owning the weights makes cost per request something your engineers can lower.
How to get up and running with Kimi K3 without the overhead of running an entire cluster.
With more than half of CLI usage driven by agents, there's strong demand for Runpod's agent-ready infrastructure.
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
Python versions carry a security clock and a performance upgrade you're leaving on the table — here's what changes when you move off an old interpreter, and when it's fine to wait.
By optimizing test architecture through pool-mode parallelization and isolated worker identities rather than increasing compute resources, the team successfully reduced merge-queue CI times by 55%.