VARNISH AI ACCELERATOR

Speed up AI training and inference. Maximize your GPU investment

High-performance caching infrastructure for large-scale AI training and inference workloads. Varnish ensures your GPUs always have fast access to the data they need — reducing wait times, cutting unnecessary data transfer costs, and helping you get more out of your investment.

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Compute has outpaced storage and network

GPU clusters can scale faster than the data infrastructure around them. The bottleneck is no longer only compute availability. It is how quickly datasets, model artifacts and training data can reach the workloads that need them.

Traditional high-performance storage can reduce I/O bottlenecks, but at high cost and with less flexibility. Object storage prioritizes cost and scale, but introduces latency, throughput, and compatibility challenges. Varnish bridges that gap.

Single TCP connection to a hyperscaler bucket can cap around 2–3 Gb/s.

First-byte latency from object storage can be around 120 ms.

A modern AI training node can need 1.5–3 GB/s of sustained dataset throughput.

GPUs wait for data

Expensive compute sits idle while workloads wait for bytes from object storage.

Repeated reads increase cost

Training runs read the same datasets repeatedly across epochs, increasing storage backend pressure and transfer cost.

Object storage is not built for GPU throughput

Object storage is durable and scalable, but not optimized to feed large GPU clusters at sustained high throughput.

Performance storage is expensive to scale

Keeping all data on high-performance storage can be costly and inflexible at AI infrastructure scale, especially when large existing datasets are expensive to migrate.

Why it's different

Why tiered caching changes the economics of AI infrastructure

AI infrastructure teams often face a bad trade-off: keep or migrate to expensive high-performance storage, or accept the latency and throughput limits of cheaper object storage. Varnish adds a caching layer between storage and compute so hot data can move faster without making every byte expensive or forcing teams to migrate large existing datasets.

Varnish Tiered Caching
Keep cold data cheap and hot data fast. Varnish adds one or more cache tiers between object storage and compute. Frequently accessed data moves closer to the workload, while scalable origin storage remains cost-efficient.
Fetch once, serve repeatedly Reduce origin reads and egress pressure Keep hot data near GPU workloads Improve GPU utilization Scale performance without putting all data on premium storage

Varnish Tiered Caching

Keep cold data cheap and hot data fast.

Varnish adds one or more cache tiers between object storage and compute. Frequently accessed data moves closer to the workload, while scalable origin storage remains cost-efficient.

Fetch once, serve repeatedly
Reduce origin reads and egress pressure
Keep hot data near GPU workloads
Improve GPU utilization
Scale performance without putting all data on premium storage

Traditional high-performance storage
Fast, but expensive to scale Purpose-built performance storage can reduce bottlenecks, but scaling it across large datasets and distributed GPU clusters can drive major cost and complexity.
High storage cost at scale Hard to justify for cold or rarely used data Expensive to replicate across locations Forces spend into storage instead of compute

Traditional high-performance storage

Fast, but expensive to scale

Purpose-built performance storage can reduce bottlenecks, but scaling it across large datasets and distributed GPU clusters can drive major cost and complexity.

High storage cost at scale
Hard to justify for cold or rarely used data
Expensive to replicate across locations
Forces spend into storage instead of compute

Object-storage-only Architecture
Scalable and cheap, but too slow Object storage works well for capacity and durability, but AI workloads often need low-latency, high-throughput data access close to GPU clusters.
Latency between storage and compute Throughput limits under heavy parallel reads Repeated origin reads across epochs Egress and transfer cost exposure

Object-storage-only Architecture

Scalable and cheap, but too slow

Object storage works well for capacity and durability, but AI workloads often need low-latency, high-throughput data access close to GPU clusters.

Latency between storage and compute
Throughput limits under heavy parallel reads
Repeated origin reads across epochs
Egress and transfer cost exposure

What it does

Fetch once. Cache locally. Serve at high speed.

Varnish Tiered Storage sits between your object storage and your compute. It pulls data from cheap, scalable cloud, on-prem or hybrid object storage — once — then keeps the hot data in fast local cache close to compute, served at sub-millisecond latency and ultra-high throughput (terabit-range). Multiple cache tiers let you balance cost and performance across your workload: hottest data lives at the edge, cooler data steps back, scalable origin stays cheap but durable. You pay for fast storage only where you need it, without staggering data-transfer costs.

Architecture Diagram

Cost benefits

Cheap, scalable origin storage

Cloud economics for cold data.

Minimize transport costs

Reduce repeated origin reads & egress.

Increase GPU utilization

Shorter training cycles, higher ROI.

Performance outcomes

Accelerate AI training cycles

Minimize I/O bottlenecks in training, maximize GPU-time spent on compute and actual training.

Remove inference cold starts

Near-instant startup for data-heavy inference workloads.

Proof at scale

Higher GPU utilization without adding more GPUs

A global high-frequency trading firm used Varnish Tiered Storage to accelerate movement of exabyte-scale data into globally distributed GPU clusters. Removing data bottlenecks while improving use of existing compute.

Thousands of GPUs Exabyte-scale storage Three globally distributed clusters

25% → 75%

GPU utilization

More compute time spent on training, less time waiting on storage.

3x

More effective use of existing compute

Without investing in more GPUs.

99.8%

Cache hit rate

Cache of last resort — reduced origin reads and egress.

1.6 Tbps

Edge-tier throughput

Terabit-range delivery close to compute.

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

Capabilities

Built for real-world AI infrastructure

Varnish AI Accelerator is built for when datasets are large, reads are parallel and compute needs predictable access to data.

01 AI-scale caching and prefetch	Designed for multi-terabyte datasets, high-throughput parallel reads and repeated access patterns common in training and inference. Configure prefetch and prefill so data is staged before compute requests it. Scale: multi-TB datasets, parallel reads Prefetch: stage data before compute asks Reuse: fetch once, serve across epochs
02 Flexible architecture and access	Works with S3-compatible or HTTP origins across cloud, on-prem and hybrid environments, using commodity infrastructure with low CPU overhead. Mount cache as a filesystem with POSIX-compatible access for existing pipelines. S3 / HTTP: origin compatibility POSIX: filesystem mount Hybrid: cloud \| on-prem \| edge
03 Customizable cache policy	Tune TTLs, eviction rules and tier-promotion logic to match workload patterns, dataset temperature and infrastructure cost goals. # cache policy: ttl: hot=24h, warm=7d eviction: lfu + size-aware promote: midtier → edge on hit-rate
04 Observability and cluster-scale operations	Track cache hit rates, throughput and operational behavior while keeping control over invalidation, locking and observability. Support distributed environments with consistent access patterns across nodes, clusters and regions. Hit rate: per tier \| per origin Throughput: Gb/s, requests/sec Invalidation: key \| prefix \| ruleset Cluster: consistent access across regions

Built for data-heavy AI environments

Varnish AI Accelerator fits environments where data has to move fast, compute has to stay fed and storage efficiency still matters.

GPU farms and neo-clouds

Maximize GPU ROI with faster data access for shared training clusters, low-latency model serving and multi-tenant infrastructure.

Autonomous systems

Accelerate sensor-heavy training pipelines and edge model delivery for autonomous vehicles, robotics and real-time decision systems.

Telcos and mobile networks

Support AI across distributed network environments where low latency, scale and data control all matter.

Financial services AI infrastructure

Accelerate AI, analytics and trading workloads by keeping hot data closer to compute, reducing repeated data movement and improving performance across distributed environments.

Media and entertainment

Move large media assets faster for generative AI, rendering, VFX and production workflows.

Healthcare and life sciences

Speed up data-intensive clinical, genomic and scientific workflows while keeping more control over sensitive data paths.

Technical architecture

A tiered data path for AI workloads

Varnish Tiered Storage creates a capacity-to-performance gradient between object storage and GPU compute. Cold data stays durable and cost-efficient. Hot data moves closer to the workloads that need it.

Object Storage

S3 / GCS / Azure / on-prem / hybrid

→

Cache of Last Resort

Capacity-biased cache close to bucket. Reduces origin reads.

→

Midtier Cache

Balanced cache per fabric. Collapses edge requests upstream.

→

Edge Cache

Performance-first cache near GPU. Serves hot shards.

→

GPU Compute

Training, inference, and data pipelines

The cache is lossy on purpose

Varnish Enterprise treats upstream storage as the source of truth. Cache nodes are allowed to forget. If cached content is lost, the next request refetches it from the tier above. This avoids time-consuming RAID-style rebuilds and helps keep reads and writes fast under heavy churn.

Designed around the fabric boundary

Edge caches can sit close to GPU fabric while midtier caches remain on standard Ethernet, helping teams reserve expensive high-performance network capacity for compute.

Stop wasting GPU capacity

Varnish AI Accelerator helps teams reduce data bottlenecks across training, inference and large-scale AI infrastructure.

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