Launch HN: Chamber (YC W26) – An AI Teammate for GPU Infrastructure

Source: Hacker News

Introduction

Hey HN, we’re Jie Shen, Charles, Andreas, and Shaocheng. We built Chamber (https://usechamber.io), an AI agent that manages GPU infrastructure for you. You talk to it wherever your team already works, and it handles tasks like provisioning clusters, diagnosing failed jobs, and managing workloads. Demo: https://youtu.be/xdqh2C_hif4

The Problem

• Platform engineers spend half their time just keeping GPU fleets running: building dashboards, writing scheduling configs, answering “when will my job start?”
• Researchers lose hours when a training run fails because they must dig through Kubernetes events, node logs, and GPU metrics across separate tools.
• Most teams have stitched together Prometheus, Grafana, Kubernetes scheduling policies, and home‑grown scripts, spending as much time maintaining this stack as using it.

The work follows repeatable patterns: triage the failure, correlate signals, decide on a remediation. If a platform exposed structured access to the full state of a GPU environment, an agent could automate these steps.

Chamber: The Solution

Chamber is a control plane that maintains a live model of your GPU fleet, including:

• Nodes and their health
• Workloads and their lifecycle
• Team structure and permissions
• Cluster topology

Every operation the platform supports is exposed as a tool the agent can call, such as:

• Inspecting node health
• Reading cluster topology
• Managing workload lifecycle
• Adjusting resource configurations
• Provisioning infrastructure

These are structured operations with validation and rollback, not raw shell commands. Adding new capabilities to the platform automatically makes them available to the agent.

Safety and Autonomy

Infrastructure automation can be risky—a wrong call can kill a multi‑day training run or cascade across a cluster. To mitigate this, Chamber implements graduated autonomy:

• Routine actions (e.g., diagnosing a failed job, resubmitting with corrected resources, cordoning a bad node) are handled automatically.
• High‑impact actions that affect other teams’ workloads or production jobs require explicit human approval.

Every action is logged with:

• What the agent observed
• Why it acted
• What it changed

How Diagnosis Works

When the agent investigates a failure, it queries:

• GPU state
• Workload history
• Node health timelines
• Cluster topology

This enables precise diagnoses, e.g., distinguishing “your job OOMed” from “your job OOMed because the batch size exceeded available VRAM on this node, here’s a corrected config.” Different root causes trigger different automated fixes.

Market Insight

Even after working on large GPU fleets at Amazon, we found that many teams cannot tell you how many GPUs are in use at any moment—the monitoring simply doesn’t exist. They’re effectively flying blind on their most expensive hardware.

Early Adoption & Pricing

We’ve launched with a few early customers and are onboarding new teams. Pricing is still being refined; we are evaluating models such as:

• Per‑GPU‑under‑management
• Tiered plans

Transparent pricing will be published once we validate the best approach for customers.

Call to Action

We’d love to hear from anyone running GPU clusters:

• What’s the most tedious part of your setup?
• What would you actually trust an agent to do?
• What’s off‑limits for automation?

We’re here all day.

Comments URL: https://news.ycombinator.com/item?id=47401766 (Points: 2)