# Archil for data-intensive computing: a shared SSD cache over your S3

Source: https://archil.com/use-cases/data-intensive

AI training, genomics, financial analysis, and analytics run on data staged from S3 onto EBS or local NVMe — expensive, hard to manage, and slow to cold-start. Archil replaces that with one shared SSD cache in front of your buckets, warm on every instance.

[ARCHIL](https://archil.com/)

[Start building](https://console.archil.com)

# Shared SSD caching, for every workload, automatically

Archil unlocks compute flexibility and efficiency by building a shared SSD cache across your fleet that eliminates cold start latencies, drives higher resource utilization, and enables containerization.

[Start building](https://console.archil.com)[Talk to us](https://cal.com/team/archil/demo)

Compute fleetStateless workers

gpu-01

gpu-02

gpu-03

cpu-04

Archil data plane

One shared SSD cache

warm

7 GB/s / clientregion-wideno hydration

Existing object storage

s3://your-company/datasets

source of truth

## Stop moving data to the compute

Today, teams use local SSD caching to reduce object storage edges like per-request charges and high time-to-first-byte latencies. Archil eliminates these edges.

The way it works today

### A cache you build and run yourself

-   Provision EBS or local NVMe on every instance and pay for peak capacity on each one — whether or not it is reading.
-   Maintain sync code that stages the right data onto the right disk, and track thousands of volumes across the fleet.
-   Size instances around the disks they carry and pin workloads to an availability zone to stay close to their volumes.
-   Every new instance sits idle hydrating its own local copy before it can do work — the tax you pay to scale out.

With Archil

### One shared cache, managed for you

-   One cache, shared by every instance in the region and backed by SSDs automatically. Pay for what you read, not what you provision.
-   Point Archil at your existing buckets. No sync code, no volumes to size, no fleet of disks to babysit.
-   Containerize freely — no locally-attached disks to size around and no AZ affinity, so the scheduler places work anywhere.
-   New instances mount a cache that is already warm and start reading immediately. No hydration, no cold start.

## Built for data-intensive workloads

Anything that keeps petabytes in object storage and reads them at high throughput from a fleet of compute runs faster on a shared cache.

### Model training

Stream shards and checkpoints to thousands of GPUs without staging datasets onto every node first.

### Genomics

Process large sequencing datasets directly from the buckets where they already live, with no copying step in between.

### Data analytics

Back Spark, Trino, and warehouse engines with a cache that serves hot Parquet to every worker at once — no rehydration.

## Local-disk speed, without the local disks

<1ms

time to first byte

warm SSD reads, without provisioning local NVMe or EBS

7 GB/s

sustained throughput

per client, to thousands of concurrent readers — no hydration

75%

lower cost than EBS

elastic, pay for what you read — not what you provision

Ready when your fleet is

## Run every workload against a cache that’s already warm.

Bring the buckets you already have. Give every GPU and worker fast shared access without provisioning local disks or waiting for data to hydrate.

[Talk to us](https://cal.com/team/archil/demo)[Read the docs ](https://docs.archil.com)