AI & DATA ACCELERATION
Why is your GPU cluster waiting on storage?
GPU clusters don't stall because the hardware is slow. They stall because the data isn't there when they need it.
A single idle GPU costs hundreds of dollars an hour. A training cluster waiting on remote object storage is a budget problem, a timeline problem, and a competitive problem at once
S3-compatible storage is cost-effective and durable. It was never designed to feed petabyte-scale training workloads at the throughput modern clusters demand.
Why this problem is getting worse
Three forces are widening the gap between storage and compute:
Training datasets are growing faster than network bandwidth
Foundation model training sets now routinely exceed petabyte scale. Standard S3 access patterns can't reliably deliver the throughput a modern GPU cluster demands.
Parallel read profiles overwhelm storage APIs
Thousands of concurrent GPU threads reading simultaneously hit S3 rate limits hard. Throttling cascades into job failures and wasted compute time.
Generic caching layers don't understand ML workloads.
Standard HTTP caches have no concept of POSIX semantics, can't interface with PyTorch or TensorFlow natively, and lack the version-pinned consistency checks that training workloads require.
What slow data pipelines actually cost your teams
The impact lands differently depending on where you sit:
Head of AI infrastructure
Training timelines stretch and model releases slip while the cluster sits idle waiting on data it fetched in the last epoch.
HPC and SRE lead
Research teams spend time manually partitioning and staging petabyte-scale datasets across local scratch disks instead of focusing on model development.
ML engineering lead
Frameworks like PyTorch and TensorFlow are tuned for fast local reads. Routing them through remote object storage introduces latency that compounds.
Why generic storage solutions don't solve this
Traditional storage architectures and standard web caches are not built to handle the massive, parallel read profiles of deep learning training sets.
Challenge 1
Parallel filesystems require specialised hardware
Platforms like GPFS or Lustre deliver throughput but need dedicated localised hardware, are complex to maintain, and can't span hybrid or multi-cloud environments.
Challenge 2
Cloud-native accelerators create zone lock-in
Services like S3 Express One Zone reduce latency but bind you to a single availability zone and offer no native POSIX interface for ML frameworks.
Challenge 3
Standard web CDNs don't speak ML
Traditional HTTP caches can't interface with PyTorch or TensorFlow natively and lack the version-pinned consistency checks that training workloads require.
Challenge 4
Uncoordinated requests trigger rate limiting
Concurrent GPU threads hitting the same S3 bucket simultaneously trigger throttling, packet drops, and cascading job failures.
Our purpose-built storage could not keep our GPUs fed. Varnish tiered storage fixed that bottleneck, and cut our storage bill while doing it.
Global high-frequency trading firm
How Varnish AI Accelerator solves it
Varnish AI Accelerator is a high-throughput data delivery layer that eliminates GPU starvation. It operates as a local read-through caching buffer between your S3-compatible object storage and GPU compute clusters, fetching datasets once and streaming them to nodes at memory speed.
01
A tiered cache fabric between storage and compute |
A three-tier topology absorbs traffic before it reaches your S3 bucket. A capacity-biased storage layer sits in front of the bucket, a mid-tier collapses redundant requests, and a local edge tier delivers hot data directly to GPU nodes at memory speed. |
02
Large objects sliced and fetched in parallel |
Gigabyte-scale model weights and dataset shards are automatically segmented into byte-range requests, fetched and cached in parallel. Memory buffers never get choked and local network throughput is maximised throughout. |
03
Cloud storage mounted as a local filesystem |
The cache mounts directly as a read-only local directory on compute nodes via FUSE. Cloud buckets appear as standard file structures. PyTorch and TensorFlow pipelines require no modification. |
04
Version-pinned consistency across distributed clusters |
Cache keys bind to unique S3 object version tags, ensuring data consistency across distributed nodes even when source files are updated. Stale files never corrupt model weights. |
The numbers
GPUs stay fed. Training completes on schedule. Research teams stop managing data infrastructure and start shipping models.
<0.5ms
1.5 Tbps
99.8%
Varnish AI Accelerator
Keep GPUs at 100% utilization. Slash object storage read latency.
A purpose-built data delivery layer engineered to eliminate the gap between object storage and GPU compute. POSIX-compatible, S3-native, and transparent to your existing ML pipelines. No rewriting pipelines. No replacing object storage. No waiting on data your cluster already fetched.
Resources and media
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Feeding GPUs at scale
What AI infrastructure teams can learn from tiered caching architectures
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How memory maps deliver 25x faster file access in Go
What should you do when you need to do a lot of disk I/O but you care about performance? One solution is to use memory maps.
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Caching fire with Varnish Enterprise & Wasabi Hot Cloud Storage
Varnish consistently reduced response times and maximized network throughput, exceeding 90% network saturation even on large object delivery.
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How to accelerate GitHub and reduce CI/CD bottlenecks at scale
If you are looking for ways to accelerate GitHub, improve GitHub Actions performance, or reduce CI/CD bottlenecks, the root issue is not simply scale.
Next steps
Talk to our team to learn more about Varnish AI Accelerator or explore more today.
