Alluxio

Open Source Data Orchestration for Analytics and Machine Learning in the Cloud
@TachyonProject is now @Alluxio! [email protected]

[Alluxio @Alluxio]

🛤️ Modernizing a data lake doesn’t have to change every user workflow.

Geely(@GeelyAutoGlobal) used Alluxio to access OSS with familiar HDFS CLI ops across HDFS/OSS, helping teams keep the same path while modernizing storage.

https://t.co/Qeu8VK7WrG

#DataInfra

[Alluxio @Alluxio]

Checkpointing is not only about saving progress. It affects restart speed too.

Alluxio AI 3.9 speeds writes and read-after-write recovery access.

https://t.co/yavzVk0jXc

#AIInfra

[Alluxio @Alluxio]

Object storage is the durable source of truth.

AI workloads need a faster path for hot data.

Alluxio caches active data near compute while keeping the original object store in place. 👇

https://t.co/A9lhwqlLEB

#AIInfra

[Alluxio @Alluxio]

🧭 Data acceleration is not one-size-fits-all.

The Alluxio + OCI benchmark looks at dedicated and co-located patterns for AI workloads.

https://t.co/7WuPrOsm0E

#AIInfra #GPU

[Alluxio @Alluxio]

Checkpoint bottlenecks aren’t always in the framework.

If writes hit object storage, latency + bursts can slow training.

Alluxio Write Cache writes near compute, then persists async.

https://t.co/9XsBE6crAB

#AIInfra

[Alluxio @Alluxio]

⏳In PyTorch training, small waits add up.

Data loading, memory copy, and CPU preprocessing can turn into real training time across batches and GPUs.

Our eBook breaks down how to improve the path from storage to GPU.

https://t.co/1Of5lFaz6U

#PyTorch #AIInfra #GPU

[Alluxio @Alluxio]

🗂️ At AI scale, data access starts with finding data fast.

Alluxio DORA skips the central master path, with workers caching data + metadata near compute.

Learn more: https://t.co/nsTzhneqij

#AIInfra #GPU

[Alluxio @Alluxio]

Why copy the full dataset before training starts?

With Alluxio, teams keep source data in place, cache hot data near GPUs, and make data available on demand.

👉https://t.co/AZ4mYuHtMc

#AIInfra #GPU

[Alluxio @Alluxio]

Serving large models across GPU clouds raises one question:

How do you get model files to GPU nodes fast?

Fireworks AI (@FireworksAI_HQ) uses Alluxio to cache hot model data near GPUs and reduce repeated pulls from object storage.

https://t.co/Zz3aTR2IgB

#Inference #AIInfra

[Alluxio @Alluxio]

♻️ AI training rereads data across epochs and trials.

With Alluxio caching near Ray, 1TB warm reads dropped from 4,200+ sec to ~208 sec.

Learn more: https://t.co/cE6DuNzZzx

#AIInfra #Ray #GPU

[Alluxio @Alluxio]

⏱️ Recommendation systems have deadlines.

RedNote (@xiaohongshu) needed models ready by 6AM, but nightly training neared 10 hours as data grew.

With Alluxio, training dropped to 5.5 hours, bringing it back within the update window.

https://t.co/7DhCvrg1VZ

#AIInfrastructure

[Alluxio @Alluxio]

Slow distributed training isn’t always a data loading problem.

Training runs fine → checkpoint starts → GPU utilization drops → training resumes.

The write path may be the bottleneck.

Read the blog: https://t.co/QkGImjBnHz

#AIInfrastructure #GPU

[Alluxio @Alluxio]

📍 GPU capacity has a geography problem.

The next available cluster may not be near the training data.

Distributed caching helps place active data closer to compute, without moving the source of truth.

https://t.co/BnZcYb2701

#AIInfrastructure #GPU

[Alluxio @Alluxio]

🧩Copying training data before every job does not scale.

@Coupang uses Alluxio distributed caching to access data from a central lake, cache hot data near compute, and improve I/O by ~40% vs cloud parallel file systems.

https://t.co/NI6d6qrJiF

#AIInfrastructure

[Alluxio @Alluxio]

🚦Not every workload needs one data path.

Batch jobs handle object storage latency. Training, inference, feature stores, and RAG need hot data closer to compute.

https://t.co/io7LTjzc5Y

#AIInfra

[Alluxio @Alluxio]

🔄 Moving from HDFS to object storage is rarely just a storage change.

@GeelyAutoGlobal used Alluxio to simplify access across HDFS and OSS, letting teams keep familiar HDFS CLI operations while supporting a flexible data lake architecture.

https://t.co/dcreZSeE7s

#DataInfra

[Alluxio @Alluxio]

Training jobs wait on more than reads. Checkpoint writes can turn latency into GPU idle time.

Alluxio AI 3.9 speeds them with POSIX Write Cache near compute. 👇

https://t.co/Q9umQ0VdRW

#AIInfra

[Alluxio @Alluxio]

🎯Not every byte in object storage needs to move closer to compute.

But hot data should.

Alluxio caches hot data near compute while source data stays in place.

https://t.co/tjcYxVyoxd

#AIInfra

[Alluxio @Alluxio]

Large-scale inference depends on getting models to GPU clusters fast.

Fireworks AI (@FireworksAI_HQ) serves 2PB+ daily; with Alluxio, replica downloads went from 20 min to 2 min and throughput reached 1 TB/s.

📌 https://t.co/6IJ3G7bSJw

#AIInfra #GPU

[Alluxio @Alluxio]

Data access changes across the ML pipeline: ingestion needs write throughput, training needs high read throughput, and inference needs low latency.

Our whitepaper breaks down how to design for each stage. ⬇️

https://t.co/FQMFaBIo2i

#AIInfra #MLOps #GPU