266 stories
Learn how to run SkyRL, an open-source reinforcement learning framework, on Amazon SageMaker HyperPod to post-train a Qwen3-VL-8B vision-language model with GRPO.
Deploy the publicly available Qwen3-TTS-12Hz-1.7B-Base text-to-speech model from Amazon SageMaker JumpStart to a fully managed, real-time endpoint, and clone a voice from a short reference clip. Cross-lingual cloning preserves the speaker's identity across languages.
A quiet day lets us discuss the work behind the scenes - now open for business!
Learn how to optimize your serverless GPU deployment on Runpod to balance latency, performance, and cost. From active and flex workers to Flashboot and.
Runpod's serverless platform keeps evolving, especially for LLM workloads. Learn what's new in 2025 and how to get the most from scalable deployments.
New to serverless? This guide shows you how to deploy a basic "Hello World" API on Runpod Serverless using Docker, perfect for beginners testing their.
Confused about spot vs. on-demand GPU instances? This guide breaks down the key differences in availability, pricing, and reliability so you can choose.
Deploy large language models like LLaMA or Mixtral on Runpod Serverless with strong privacy controls and no infrastructure headaches. Here’s how.
Discover how to deploy Meta's Llama 3.1 using Runpod's new vLLM worker. This guide walks you through model setup, performance benefits, and step-by-step.
Learn how to run vLLM on Runpod’s serverless GPU platform. This guide walks you through fast, efficient LLM inference without complex setup.
Learn how to build an OCR pipeline with Runpod Serverless and Hugging Face models, for processing receipts, invoices, and scanned documents at scale.
Curious how local LLM deployment stacks up? This post explores benchmarking strategies, optimization tips, and what DevOps teams need to know about.
Better Forge is a new Runpod template that lets you launch Stable Diffusion pods in less time and with less hassle. Here's how it improves your workflow.
I tested TypeSafe AI’s Jev on 3,080 classification tasks to see how its accuracy, latency, calibration, and confidence compare with LLMs — and whether it works as a practical decision layer for AI systems. The post Jev vs.
The build side of running voice AI locally: what fits on one 24GB card, which runtime to use per stage, and what the latency actually looks like on a phone call.
Hermes moved its memory providers out of core and into the plugin catalog. Hindsight went first. Here is what changed, what you need to do about it, and where updates come from now.
The AWS WhisperX Deep Learning Container packages Whisper, wav2vec2 forced alignment, and speaker diarization into a GPU-ready image. Learn how to deploy it to Amazon SageMaker AI real-time and asynchronous endpoints for word-level, speaker-labeled transcription, plus the production details that…
As deep learning models move rapidly from research prototypes into core enterprise infrastructure, the demand for practical, end-to-end PyTorch expertise has never been higher. Building robust neural networks requires more...
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