INDUSTRY
Varnish for AI Infrastructure
AI infrastructure performance is no longer only about how much compute you can buy. As training, inference and AI development pipelines scale, data movement becomes the bottleneck. Varnish helps AI teams keep relevant data closer to where it is used, reduce repeated transfers, accelerate software and model delivery, and get more value from existing GPU capacity.
Challenges
AI infrastructure has a data movement problem.
AI teams are scaling compute faster than storage and networks can keep up. GPU clusters, object storage, distributed CI/CD systems and model-serving environments all depend on fast, reliable access to large volumes of data. But when the same datasets, containers, packages, model files and artifacts are pulled repeatedly across regions, clouds and clusters, infrastructure cost rises while performance becomes less predictable.
Varnish helps reduce that waste by caching data, artifacts and software dependencies close to the workloads that need them. The result is faster access, lower backend pressure and better utilization of the infrastructure already in place.
Critical challenges Varnish helps AI infrastructure teams address
GPU capacity is too expensive to leave waiting
AI infrastructure teams invest heavily in compute, but GPUs only create value when they are actively processing work. If training jobs, inference services or data-heavy pipelines wait for storage, network or repeated object retrieval, utilization drops and ROI suffers.
Lower GPU utilization, longer training cycles and reduced return on expensive compute capacity.
Object storage is scalable, but not always fast enough
Object storage gives AI teams durable, cost-efficient capacity, but direct access from compute-heavy workloads can introduce latency, throughput and compatibility challenges. Keeping all hot data on expensive performance storage is rarely sustainable.
Storage cost grows quickly when performance tiers are used as a workaround for access latency.
Agentic software development creates bottlenecks in CI/CD pipelines
AI-assisted and agentic development increases the volume and speed of software changes moving through CI/CD. More builds, more dependency requests and more automated package pulls can overload registries, trigger rate limits and introduce supply chain risks before teams have time to review what enters the environment.
Increased build times, unpredictable transfer costs, and exposure to malicious and vulnerable packages.
Data infrastructure is spread out and expensive to migrate
Large enterprises rarely have all AI data in one place. Datasets, models and supporting assets are often spread across regions, clouds and legacy environments. Moving everything to where compute runs is expensive, slow and operationally risky. A smarter approach is to place cache closer to the workloads that need the data.
Rising migration cost, slower time to production and less flexibility when scaling AI workloads across regions and environments.
Solutions for AI Infrastructure
Keep hot AI data close to compute
Sit between object storage and GPU compute. Fetch data once, cache it locally and serve hot data at high speed where training and inference workloads run — without giving up durable, cost-efficient object storage as the system of record.
Accelerate AI build and runtime artifacts
Cache and accelerate containers, model packages, Python, npm, Maven, Go, OS packages and Git repositories close to CI/CD workers, clusters and runtime environments across regions.
Control what enters AI development environments
Add an enforcement point in the artifact request path so teams can apply policy before unapproved, vulnerable or newly published dependencies spread through pipelines and runtimes.
PRODUCTS
Varnish AI Accelerator
Varnish AI Accelerator sits between object storage and GPU compute. It fetches data once, caches it locally and serves hot data at high speed where the workload runs. Teams can keep durable, cost-efficient object storage as the system of record while using tiered cache to deliver the performance required for training and inference.
- Accelerate AI training pipelines and data-heavy inference workloads
- Serve hot data close to GPU farms and neoclouds
- Support HFT, real-time analytics and autonomous systems
- Fit genomics, research and media workloads alongside object storage
PRODUCTS
Varnish Virtual Registry
AI infrastructure depends on more than datasets. Every training run, model build, deployment and runtime environment depends on software artifacts moving reliably through the organization. Varnish Virtual Registry caches and accelerates registries for build and runtime artifacts close to CI/CD workers, clusters and runtime environments.
- Accelerate Docker images and OS packages across regions
- Cache Python, npm, Maven and Go dependencies near runners
- Speed up Git and Git LFS for distributed CI/CD environments
- Support self-hosted runners, Kubernetes and multi-region teams
PRODUCTS
Varnish Artifact Firewall
AI teams move fast, but speed increases exposure when dependencies, containers and packages are pulled automatically across pipelines and runtime environments. Varnish Artifact Firewall adds an enforcement point in the artifact request path, helping teams apply policy before unapproved, vulnerable or newly published dependencies spread through distributed environments.
- Govern AI software supply chain and dependency access
- Enforce CI/CD policy across distributed pipelines
- Apply package delay and vulnerability-based access rules
- Support distributed development teams with consistent policy
FAQ
For many large-scale AI environments, the bottleneck is no longer only compute. Storage access, repeated data movement, object retrieval, software artifacts and network paths can all prevent GPUs from staying fully utilized.
Varnish keeps frequently used data close to the workloads that need it. By reducing repeated origin reads and serving hot data from cache, AI teams can shorten wait times and keep more GPU time focused on training or inference.
No. Object storage remains the durable, cost-efficient system of record. Varnish adds a high-performance caching layer between storage and compute so teams do not need to keep all data on expensive performance storage.
Yes. Varnish is designed for cloud, on-prem and hybrid environments where teams need more control over where data moves and where it is served.
AI infrastructure depends on software artifacts as well as datasets. Varnish Virtual Registry helps cache and accelerate containers, packages, Git repositories and other artifacts, while Varnish Artifact Firewall adds policy enforcement for dependency access.
The primary product is Varnish AI Accelerator for data access between object storage and GPU compute. Varnish Virtual Registry is relevant for artifact and registry acceleration. Varnish Artifact Firewall is relevant for software supply chain control across AI development pipelines.
The Varnish Book
The Varnish Book is a practical book full of tips and best practices for getting the most out of your Varnish setup and reaching new heights in your caching operations, whether you’re new to Varnish or an experienced pro.
Dig Deeper
Blog post
Feeding GPUs at Scale: What AI Infrastructure Teams Can Learn from Tiered Caching Architectures
Varnish is a high-throughput, multi-tier caching solution that can eliminate object storage bottlenecks and triple GPU utilization for enterprise AI infrastructure teams managing large-scale training clusters.
Case Study
How a global high-frequency trading firm increased GPU utilization while reducing storage cost
Performance gains as massive scale, without adding more GPUs or keeping all data on expensive storage.
Stop wasting GPU capacity.
Let Varnish help remove data, artifact and dependency bottlenecks across your AI infrastructure.
