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# Democratizing Managed Lustre with lower cost and frictionless development

**[Google Cloud](https://daily.dev/sources/gcp)** · 7 min read · 0 upvotes · 0 comments

## Summary

Google Cloud announced a new Dynamic Tier for Managed Lustre priced at 6 cents/GB per month, offering sub-millisecond latency for hot data in a single namespace alongside a slower Capacity Pool (HDD) tier. The tier targets AI/HPC workloads such as multi-epoch training, checkpointing, and interactive developer tasks like git cloning and compiling, claiming up to 4x better responsiveness than alternative distributed filesystems and 67% improved aggregate throughput for high-concurrency reads. The post includes fio benchmark scripts and commands for testing untar, git clone, Python compilation, and parallel PyTorch imports across thousands of processes. A second part of the series will cover upcoming object storage integration features.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://cloud.google.com/blog/topics/developers-practitioners/democratizing-managed-lustre-with-lower-cost-and-frictionless-development>

## Questions this post answers

### What is the cost of Google Cloud Managed Lustre's Dynamic Tier?

Google Cloud Managed Lustre's Dynamic Tier costs 6 cents per GB per month with a single flat fee. There are no separate charges for disk media types, data movement within the namespace, or metadata IOPS. It provides sub-millisecond latency for hot data stored in a High-Performance Cache (SSD), while a Capacity Pool backed by Hyperdisk Throughput handles colder data with 10-30 ms average read latency.

_Teams weighing storage tiers for AI pipelines can track pricing and performance changes like this on daily.dev._

### How fast is Google Cloud Managed Lustre for developer tasks like git clone and compiling code?

A 20-worker parallel git clone of the Python repository takes about 40 seconds, compiling Python takes roughly 200 seconds, and untarring a Linux kernel tarball takes about 2 minutes, which is 4.7x faster than alternative file solutions. Average read latency is around 300 microseconds, giving a local-disk feel for interactive work on the same shared storage used for large training datasets.

_Developers evaluating storage backends for build and training pipelines follow benchmarks like this on daily.dev._

### How does Managed Lustre handle thousands of nodes reading the same file during GPU cluster startup?

Managed Lustre improves aggregate throughput by 67% over alternative file solutions when thousands of worker nodes simultaneously read the same file, such as a shared model checkpoint or container layer, avoiding localized hotspotting that leaves GPU clusters idle. It can import libraries like PyTorch across more than 4,000 processes in under 60 seconds using parallel loading.

_Engineers optimizing GPU cluster startup times keep an eye on storage benchmarks like these via daily.dev._

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