ALLUXIO COMMUNITY OFFICE HOUR
Today’s conventional wisdom states that network latency across the two ends of a hybrid cloud prevents you from running analytic workloads in the cloud with the data on-prem. As a result, most companies copy their data into a cloud environment and maintain that duplicate data. All of this means that it is challenging to make both on-prem HDFS data accessible with the desired application performance.
In this talk, we will show you how to leverage any public cloud (AWS, Google Cloud Platform, or Microsoft Azure) to scale analytics workloads directly on on-prem data without copying and synchronizing the data into the cloud.
In this Office Hour, we will go over:
- A strategy to embrace the hybrid cloud, including an architecture for running ephemeral compute clusters using on-prem HDFS.
- An example of running on-demand Presto, Spark, and Hive with Alluxio in the public cloud.
- An analysis of experiments with TPC-DS to demonstrate the benefits of the given architecture.
ALLUXIO COMMUNITY OFFICE HOUR
Today’s conventional wisdom states that network latency across the two ends of a hybrid cloud prevents you from running analytic workloads in the cloud with the data on-prem. As a result, most companies copy their data into a cloud environment and maintain that duplicate data. All of this means that it is challenging to make both on-prem HDFS data accessible with the desired application performance.
In this talk, we will show you how to leverage any public cloud (AWS, Google Cloud Platform, or Microsoft Azure) to scale analytics workloads directly on on-prem data without copying and synchronizing the data into the cloud.
In this Office Hour, we will go over:
- A strategy to embrace the hybrid cloud, including an architecture for running ephemeral compute clusters using on-prem HDFS.
- An example of running on-demand Presto, Spark, and Hive with Alluxio in the public cloud.
- An analysis of experiments with TPC-DS to demonstrate the benefits of the given architecture.
Videos:
Presentation Slides:
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Videos
Fireworks AI is a leading inference cloud provider for Generative AI, powering real-time inference and fine-tuning services for customers' applications that require minimal latency, high throughput, and high concurrency. Their GPU infrastructure spans 10+ clouds and 15+ regions, serving enterprises and developers deploying production AI workloads at scale.
With model sizes reaching 70GB+, Fireworks AI faced critical challenges: eliminating cold start delays, managing highly concurrent model downloads across GPU clusters, reducing tens of thousands in annual cloud egress costs, and automating manual pipeline management that consumed 4+ hours weekly. They chose Alluxio as their solution to scale with their hyper-growth without requiring dedicated infrastructure resources.
In this tech talk, Akram Bawayah, Software Engineer at Fireworks AI, and Bin Fan, VP of Technology at Alluxio, share how Fireworks AI uses Alluxio to power their multi-cloud inference infrastructure.
They discuss:
- How Fireworks AI uses Alluxio in its high-performance model distribution system to deliver fast, reliable inference across multiple clouds
- How implementing Alluxio distributed caching achieved 1TB/s+ model deployment throughput, reducing model loading from hours to minutes while significantly cutting cloud egress costs
- How to simplify infrastructure operations and seamlessly scale model distribution across multi-cloud GPU environments
