If you ask my friends and family to describe me, they’d probably say I’m someone who enjoys taking things apart just to understand how they work. They wouldn’t be wrong. I’ve spent my entire life obsessed with technology.
First as a kid experimenting with computers, now as someone who has the privilege of building the infrastructure that powers AI at global scale. Day to day my world is thousands of GPU hosts, power budgets, firmware versions, network fabrics, and the endless puzzle of keeping everything running flawlessly.
I help lead the engineering team that is responsible for the installation and stand-up of our infrastructure as a service (IaaS) layer. It is the foundation upon which every model is trained and every inference request is served.
At the heart of that mission is one deceptively simple question: What does it actually take to bring a node to life, and keep it healthy for years afterward?
That’s where Nscale’s Fleet Operations comes in and why automation, observability, and intelligent tooling have become essential to operating infrastructure at the scale we’re building.
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Automated discovery and ingestion
A node’s journey starts long before any customer uses it. Once hardware arrives at the data center, it's racked, powered, and cabled. A few years ago, bringing hundreds of GPU hosts online meant teams of engineers manually running scripts, tracking spreadsheets, and validating machines one by one. That approach works at a tiny scale but we’re well beyond tiny.
With Fleet Operations, the moment a node appears in our data center management system, it’s automatically ingested. The system immediately begins a structured, automated workflow:
- BIOS and firmware validation
- BMC configurations
- Network configuration
- Burn-in testing and performance benchmarking
At scale, manual processes break down. Our tooling captures the institutional knowledge of seasoned infrastructure engineers but executes it with the reliability and parallelism only automation can sustain.
Fleet Operations isn’t a single tool. It’s an ecosystem made up of various parts.
- Control Center is where we see every job, every node, and every workflow, with granular dashboards for real-time insight.
- Observability Platform overlays telemetry across the entire stack.
- Radar API closes the loop by pushing and receiving service events and creating automated ITSM integrations and surfacing issues instantly where operators need them.
This triad gives us automation at the rack level and intelligence across the fleet.
Fast fix loops
Not every node passes validation on the first go. When something fails, Fleet Operations produces a precise, machine-readable list of symptoms. Then, if we identify common error patterns, we are able to build automations around them.
For example, if a transceiver is faulty, the system no longer waits for a human to read a log. It automatically raises a case with our data center engineering team, requests the replacement, and then retests the node after repair. This ability to tighten the loop between detection and resolution has transformed our operational velocity.
What used to take days can now happen in minutes at scale, and asynchronously across hundreds of machines.
Observability and automated lifecycle management
Once a node is live, the work isn’t over.
Our observability stack tracks metrics across compute, network, storage, and orchestration layers. And because Fleet Operations ties into that telemetry, we get a unified picture of each machine from day one to end-of-life.
That longitudinal understanding lets us catch, for instance:
- A drive showing early signs of degradation
- A networking component performing slightly below baseline
- A GPU card experiencing thermal variance affecting workloads
Armed with that data, we can act quickly to repair it. For example, one of the biggest operational challenges in managing GPUs is drift. A node deployed on day one might arrive with one firmware build; a node deployed six months later might look nearly identical but behave slightly differently because of upstream firmware changes.
Across tens of thousands of nodes, that inconsistency becomes a bigger challenge.
Fleet Operations solves this with automated lifecycle management. Every time a node becomes inactive, maybe because a customer has finished using it, the system rechecks the firmware versions, BIOS configurations, network performance, and any deviations from our gold standard.
If something’s out of date, the system automatically patches and updates it. No ticket required. No engineer hunting down a device manually.
Automation like this is powerful, but we don’t blindly trust it. Every new workflow starts with human approval gates. As confidence grows, we gradually remove them. This phased autonomy ensures we never let automation outpace reliability. The result is fleet consistency, which is the bedrock of reliable GPU performance.
Why it matters
The first time we watched a batch of nodes run end-to-end through Fleet Operations, I remember thinking: This is the inflection point. What once took days to complete was now happening reliably, repeatedly, and at scale.
And that’s the real story: the lifecycle of a node is no longer a manual journey. It’s a well-orchestrated, intelligence-driven system that enables us to deliver the reliability, uptime, and performance our customers count on.
As we bring hundreds of thousands of GPUs online, Fleet Operations isn’t just helping us keep up, it’s helping us stay ahead.
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