DDN Infinia: The Data Engine for Accelerated Inference
Get more productive work from your GPUs and available power.
DDN is Trusted By:
Built for Inference Economics
Infinia helps neo-cloud providers (NCPs), AI natives, and enterprises get more productive work from their GPUs and available power. Users get answers sooner. Each token costs less, because GPUs reload context instead of recomputing it. Unlike traditional storage vendors DDN does not force you to make tradeoffs between performance and economics.
Nebul, a European AI cloud, cut its cost per million input tokens by reloading context instead of recomputing it.
More input tokens per second with the same GPUs, model, and workloads.
Against its own cache, Nebul’s users got responses sooner on the same GPUs.
AI is ultimately energy-bound. Maximize tokens per watt to scale economically and sustainably on existing GPUs.
Challenges
Where Inference Loses Margin and How Infinia Helps
AI agents and long prompts carry more context in every request. That context, called the key-value (KV) Cache, outgrows GPU memory. When the data layer cannot return it fast enough, GPUs recompute it. Users wait longer for the first token, and each token costs more. Infinia’s unique architecture lets GPUs reload more context instead of recomputing it.
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Recomputed Context:
GPUs rebuild context they already processed. -
Time to First Token:
Users wait longer for the first token on long prompts. -
Slowdowns at Scale:
Performance from a small trial drops on hundreds of nodes. -
Noisy Tenants:
One tenant’s spike slows the others and puts service levels at risk.
One Native Key-Value Store
Teams buy and run fewer infrastructure tiers. Infinia’s unique architecture uses one native key-value store to serve KV Cache, object data, and file data on one data engine.
Built into NVIDIA NIXL
One integration in NIXL, NVIDIA’s data transfer library, covers Dynamo, vLLM, SGLang, and LMCache. DDN is the first storage vendor whose integration ships in NVIDIA’s official NIXL package.
KV Cache Service for Faster First Tokens
Users get the first token sooner, and each token costs less. Infinia stores KV Cache and returns it to GPU memory over remote direct memory access (RDMA).
Secure Multi-Tenancy with Fair Share
Serve many tenants on one cluster. The tenant is part of every key, so every data service inherits tenant isolation. Fair share gives each tenant its assigned share.
Proven Scale and Availability
Infinia grows with the customer without a rearchitecture. It runs in production on a 300+ node cluster and is tested to 500 nodes. It provides eleven nines data durability.
Core Capabilities
The Data Engine for Faster, Lower-Cost Inference
Infinia is built on a native key-value store. It removes protocol translation, gateways, and metadata servers from the data path. Each tenant stays isolated, and Infinia grows with the customer without a rearchitecture.
What Customers Are Saying
Use Cases
Long-Context and Agentic Inference
Agents and long prompts carry more context at every step. Infinia stores that context so GPUs reload it, and users get the first token sooner.
Fast S3 for RAG and AI Data
Applications built for Amazon S3 run on Infinia with no code changes. RAG pipelines and KV cache share one data engine.
Multi-Tenant AI Services
Serve many paying tenants on one cluster. Each tenant stays isolated, and a burst from one tenant does not starve the others.
Sovereign AI
Keep AI workloads and data inside the sovereign environment, with per-tenant encryption keys, per-tenant identity, and tenant isolation in every key.
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