What Managed Lustre is, and why it's worth bringing up
Managed Lustre feeds data to GPU clusters fast enough to keep them fully utilized — up to 1.5 TB/s, cutting training time 50–70% and pushing GPU utilization to 95%+. It's fully managed by Google Cloud and powered by DDN, so there's zero DevOps for the customer. Think of it as the performance layer that sits between GPUs and Cloud Storage: Google Cloud's high-performance AI storage answer for any customer whose GPUs are idling while they wait on data.
Trigger phrases from the customer
If a customer says any of these, Managed Lustre is worth a follow-up question.
"Our GPUs are sitting idle waiting on data."
"Training is taking longer than it should."
"We're running A100s or H100s at scale."
"We've got 10TB+ of training data to move."
"We run genomics, weather, or financial simulations."
"Cloud Storage can't keep up with our GPU cluster."
Check 2 or more to flag a Qualified Opportunity
Your script, start to close
- What GPU hardware are you running, and how large are your datasets?
- Where's your training data stored today, and how is it getting to the GPUs?
- Do you know roughly what GPU utilization you're seeing during training runs?
- Have you had to slow down or delay a training run because of data loading?
- "We already use Cloud Storage." — Managed Lustre sits alongside it as a performance layer for the GPU training path, not a replacement.
- "Sounds like more infrastructure to manage." — It's fully managed by Google Cloud and powered by DDN — zero DevOps on the customer's side.
- "Is this worth the cost?" — Idle GPU time is usually the more expensive problem; this is priced against that idle time, not against storage alone.
Submit Lead to DDN
Send the prospect straight to the DDN alliances team. Include whichever qualifiers applied above.