Hub

GPU Cloud Deployment Without Leaving Your IDE — Audry Hsu, RunPod

Jun 9, 2026 · 20:19

Audry Hsu of RunPod introduces Flash, a Python SDK that deploys GPU cloud functions from a developer's IDE with a single decorator, eliminating the slow iteration cycle of commits, Docker builds, and server allocation. She demonstrates hot reload, swapping Stable Diffusion XL Turbo for DreamShaper instantly, and a pipeline that chains Qwen 3 for prompt generation, DreamShaper for image rendering, and Nano Banana 2 for photo composition. RunPod's serverless H100 pricing is $0.00116 per second, charged only during active inference. Hsu recommends starting with pods for experimentation and switching to serverless when scaling to hundreds of workers across data centers.

Under 5 minutes to a deployed LLM endpoint — Audry Hsu, RunPod

Jun 7, 2026 · 13:26

Audry Hsu of RunPod presents a cloud AI infrastructure platform that lets developers deploy LLM endpoints in under five minutes. RunPod originated from two failed crypto mining rigs in a basement in 2022; the founders offered the GPUs for free on Reddit in exchange for feedback, and the company now has 500,000 developers and $120 million in annual recurring revenue. The demo shows selecting a model from the Hub, configuring the context window, and deploying a serverless endpoint on H100s. The first request queues for 41 seconds due to cold start (container initialization and model download), while subsequent requests execute in about 1.5 seconds. Users pay only while a worker handles a request, making serverless ideal for bursty or batch workloads with autoscaling up to 15 workers.

Your Coding Agent Should Do AI System Engineering — Ben Burtenshaw, Hugging Face

May 21, 2026 · 18:25

Ben Burtenshaw from Hugging Face demonstrates how coding agents can take on AI systems engineering tasks—writing CUDA kernels, fine-tuning models, and running multi-agent research labs—by leveraging skills and the Hugging Face Hub. He shows a 1.88x speedup on H100s with an RMSNorm kernel written by Claude Code, and a fine-tuned Qwen3 0.6B achieving 35% on LiveCodeBench. Skills compress years of specialization into hours by turning zero-shot tasks into few-shot workflows. For multi-agent research, a Planner generates hypotheses from papers, Workers implement them as training scripts, and a Reporter monitors results via the open-source Trackio dashboard, with all jobs running on Hub compute. The key is exposing open primitives like kernels, Trackio, and HF jobs as agent-controllable tools.