AI SOLUTIONS
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AI INFERENCE
More tokens. Faster first tokens.
More concurrent users.
Same GPUs.
Inference economics and capacity economics in one platform. Persist context instead of recomputing it, feed weights over RDMA, and serve high-concurrency small-file patterns on an elastic metadata engine, without paying flash prices for a corpus your users rarely touch.
✓ RDMA shipping now
✓ KV cache writeback
✓ Elastic metadata for high concurrency
THE CURRENCY OF INFERENCE
1
Time to First Token
What the user actually feels. Persisted context means long sessions resume instead of prefilling again.
2
Tokens per GPU
Output you can sell from silicon you already own. Every recomputation is throughput you paid for and threw away.
3
Concurrent Users
Density decides revenue per rack. High-concurrency small-file access is a metadata problem before it is a bandwidth problem.
4
Model Load Time
Cold starts and model switches stall the fleet. Weights stream over RDMA with the CPU out of the path.
Training is measured in time to convergence. Serving is measured in these four, every second, forever.
80%
of the neocloud market will be inference workloads by 2030
THE MARKET IS SHIFTING UNDER YOU
Training workloads lead GPU-as-a-service revenue today, but the balance is shifting fast. As it does, the metrics that decide who wins shift with it, from time to convergence to time to first token, tokens per GPU, and concurrent users per rack.
Source: ABI Research, neocloud market trends
WHAT YOU ARE PROBABLY SEEING
Your model remembers. Your infrastructure forgets.
These three symptoms almost always trace back to the same root cause: context and retrieval data living somewhere too small, too slow, or too expensive to hold what serving actually needs.
It reads like an efficiency problem on the infrastructure review. It shows up as a revenue problem on the P&L.
You are paying to prefill the same context twice
When cache capacity runs out, the model recomputes context it already built. That is GPU time billed for work that was already done once.
Latency gets unpredictable as concurrency grows
Time to first token holds fine in testing, then spikes under real production load. Tail latency, not average, is what your customers feel.
One busy tenant degrades everyone else
Bursty demand collapses cache locality across a shared fleet, so tenant density becomes a risk instead of a revenue lever.
WHAT THIS MEANS FOR THE BUSINESS
Inference is a margin business. Cost per token is the scoreboard.
Training is a project. Serving runs forever, so every inefficiency compounds every day. The levers that matter are recomputation avoided, tenants per fleet, and how much of your footprint sits on premium media it does not need.
Cost Per Token Served
Serving runs forever. Every point of storage inefficiency compounds daily, and cost per token is the number finance acts on.
Don’t Recompute Twice
Context rebuilt after an eviction or restart is compute billed twice for work the user already paid for.
Latency Is the Product
Time to first token is what your customers actually experience. It is an SLA commitment, not a benchmark.
Multi-Tenancy Enables Revenue
How many tenants you can safely put on one serving fleet is a direct input to revenue per rack.
Power Is the New Bottleneck
Performance per watt on the storage tier is headroom you can redeploy into GPUs.
Sell the Corpus, Too
Tenants land their corpus over S3 and serve RAG against it in place. One capacity pool, priced as more than one product.
Flash Price Exposure
An all-flash footprint prices your margin against the SSD market. Mixed-fleet tiering takes that volatility off the table.
A Moat, Not a Commodity
Everyone rents the same GPUs. Tighter latency SLAs and more tenants per fleet is where the differentiation actually lives.
FIVE WORKLOADS, ONE NAMESPACE
The hotter the workload, the smaller it is.
That inverse relationship is the whole argument. Sizing your entire inference footprint for the hottest few percent of it is how storage ends up consuming a third of the infrastructure budget.
WORKLOAD
HOTCOLD
VDURA ANSWER
KV Cache
Tiny, brutally hot · sub-ms latency
Flash. Session context persists across restarts, so long conversations never recompute and first-token latency stays inside SLA.
Active Model Weights
Hot · GPU-saturating reads
Flash + RDMA. Direct GPU-to-storage reads, no CPU in the path. Exceeds NVIDIA and AMD per-GPU throughput specs.
Model Library
Every version and variant · warm
Both tiers + snapshots. Instant, space-efficient snapshots of every version. Roll back a bad deploy in seconds without copying terabytes.
RAG Corpus
Petabyte-scale · sparsely read
HDD + automatic tiering. Bulk corpus on high-capacity disk; content your users actually retrieve is promoted to flash automatically.
Sessions & Archive
Logs, telemetry, history · cold, growing
Lowest-cost tier. End-to-end encryption, 11+ nines durability. This data grows without bound; the cost of keeping it should not.
Dot size = relative capacity. The hotter the workload, the smaller it is.
ONE FILE SYSTEM · ONE NAMESPACE · ONE CONTROL PLANE · PER-TENANT ISOLATION ON EVERY WORKLOAD
KV CACHE AND CONTEXT
Recomputing context is the most expensive thing your fleet does twice.
A long conversation, a large document, an agent with history: all of it lives in KV cache. When that cache evicts or a pod restarts, the GPU recomputes context the user already paid for once. At scale that is a permanent tax on both latency and margin.
VDURA gives KV cache a persistence tier with a real SLA. Context survives eviction and restarts, so long sessions resume instead of recomputing, and time to first token stays inside the number you promised your customers.
Talk through your serving profileTIME TO FIRST TOKEN, LONG CONTEXT
Cache evicted, context recomputed
GPU pays twice
Context persisted, session resumes
inside SLA
Same model, same GPUs. The only difference is whether context had to be rebuilt from scratch.
WHY TOKENOMICS ALONE IS NOT ENOUGH
Optimizing tokens on top of broken storage economics still leaves you buying the flash twice.
A cache-acceleration layer improves your token math. It does nothing about the petabytes underneath it, so you end up funding a fast tier and a capacity platform separately, and moving data between them. VDURA solves both halves on one platform.
ACCELERATION LAYER ONLY
—
Accelerates the cache tier, then hands the petabytes underneath to a separate capacity platform.
—
You fund a fast tier and a bulk tier as two purchases, with two things to operate.
—
Data has to move between the tiers, and that movement is latency and engineering time.
—
Token math improves. Cost per usable terabyte does not.
VDURA: BOTH ECONOMIES, ONE PLATFORM
✓
One namespace spans the hot context tier and the petabyte corpus. No second stack to buy.
✓
Flash sized for throughput and latency; capacity sized independently for scale.
✓
Context-Aware Tiering places data on the right media as access patterns shift, in place.
✓
Inference economics and capacity economics move together, on one control plane.
THE SERVING PATH, REQUEST TO RESPONSE
Four places storage decides your latency.
HYDRA ARCHITECTURE
Throughput comes from parallel node count, not flash capacity.
This is the sentence that changes the purchase order. Flash is sized for the throughput and latency your hot path needs; capacity is sized separately for scale. Every DirectFlow client reads from storage nodes in parallel over RDMA, with the CPU out of the path, so first-token latency does not degrade as the corpus grows.
2.7 TB/s
per rack, all-flash
12 nines
durability with MLEC
1,500+
nodes, one namespace
6+ nines
availability
RDMA Data Paths
→
Direct GPU-to-storage transfers with the CPU out of the path, on NVIDIA ConnectX-7 or AMD Pensando 400G fabrics.
KV Cache Writeback
→
A persistence tier for context, so sessions survive eviction and pod restarts instead of recomputing.
VeLO Elastic Metadata Engine
→
Scales metadata throughput on demand for the small-file and high-concurrency access patterns that serving and RAG retrieval generate.
Context Cache Tiering
→
A tiering framework for context and retrieval data, promoting what is actually read to flash as access patterns shift.
Snapshots for Model Ops
→
Instant, space-efficient snapshots for versioning, A/B, and blue-green rollback without copying terabytes.
S3 and Performance
→
Sustained S3 throughput on the same data plane and namespace as file, no gateway, no copy, plus native object tagging for lifecycle and tiering policy.
Multi-Tenancy
→
Per-tenant QoS, namespaces, encryption keys, and VLAN isolation as first-class capabilities, not bolt-on tooling.
REST API
→
Provisioning, quotas, snapshots, and tenant lifecycle through REST, with Terraform, Ansible, and Crossplane providers.
Resiliency: MLEC
→
Multi-level erasure coding plus client-side erasure coding. Failure domain is a VPOD, not a server, at up to 12 nines durability.
Open Hardware and Support
→
Commodity servers and your choice of drive and fabric vendor, backed by a direct support portal and the VDURAbility service model.
Observability
→
Real-time health, per-tenant capacity and performance telemetry, and the precise scope of any event, with the MyVDURA portal for fleet-level insight.
End-to-End Encryption
→
Tenant-per-volume AES-256 from client through transit to rest, with KMIP key management.
AGENTIC AI CHANGES THE SHAPE OF THE PROBLEM
One user question. Dozens of model calls.
A chatbot does one round trip. An agent plans, calls tools, retrieves, reflects, and retries, carrying state the whole way. The storage profile changes with it: more retrievals per request, far more context to keep warm, and history that has to persist across steps and sessions.
MORE CALLS
Multi-step reasoning
A single request fans out into planning, tool calls, and reflection, each one touching context and retrieval again.
MORE CONTEXT
State that has to persist
Agent memory spans steps, sessions, and days. Losing it mid-task means starting the reasoning chain over.
MORE RETRIEVAL
Grounding on every step
Agents retrieve continuously rather than once, pushing far more random reads at the RAG corpus.
MORE HISTORY
Traces you have to keep
Every decision an agent makes is something you need to audit, evaluate, and improve against later.
DEPLOY ON YOUR TERMS
Software-defined, so it fits the fleet you already run.
PROTOCOLS AND INTEGRATIONS
DirectFlow
POSIX
NFS
SMB
S3
Kubernetes CSI
RDMA
REST API
Terraform
Ansible
KMIP
Containerized serving stacks get persistent volumes through the native CSI plug-in with no custom scripting. Provisioning, quotas, snapshots, and tenant lifecycle all run through REST APIs, with Terraform and Ansible for infrastructure as code.
HOW IT SHOWS UP IN YOUR RACK
Software-defined on open hardware
The platform is software. It runs on commodity servers and standard Ethernet or InfiniBand, so you keep your choice of server, drive, and fabric vendor.
Certified building blocks
V5000 flash and capacity nodes ship pre-integrated and jointly validated, so a serving cluster is a known quantity before it ships. Start at 6 nodes and add throughput or capacity independently.
Operational in a day, upgraded in place
Deployment is measured in hours. Expansions, software updates, and rebuilds happen online while you keep serving traffic.
MULTI-TENANCY ENABLES REVENUE
Inference is where multi-tenancy stops being a feature and starts being the business model.
One serving fleet, many customers, hard isolation guarantees between them. How many tenants you can safely put on the same fast tier is a direct input to revenue per rack.
Per-Tenant QoS
One noisy tenant cannot spend another tenant’s latency budget.
Namespace Isolation
Hard boundaries between customers on the same physical fleet.
Keys Per Tenant
AES-256 end-to-end with KMIP, scoped to the tenant, not the cluster.
API-Driven Onboarding
Tenant lifecycle through REST and IaC, so adding a customer is not a project.
WHO SERVES ON VDURA
Different applications. The same four numbers.
AI Clouds and Factories
Inference sold by the token, where latency is the product and tenants per fleet decides revenue per rack.
GPU CAPACITY PROVIDERS
Enterprise AI
Internal assistants and agents grounded in corporate knowledge, where the RAG corpus dwarfs the model itself.
RAG AND AGENT PLATFORMS
Life Sciences
Inference over imaging and genomics archives, with billions of small files and long retention requirements.
RESEARCH AND CLINICAL AI
Government and Defense
Sovereign and air-gapped serving under strict security, encryption, and data residency requirements.
SOVEREIGN AND CLASSIFIED
GO DEEPER
Resources for inference infrastructure teams.
REFERENCE ARCHITECTURE
AMD Validated: Storage for GPU-Accelerated AI
A 32-node Scalable Unit with 256 AMD Instinct GPUs, 5 PB usable per rack, on a no-single-point-of-failure 400 GbE fabric.
Read the design →
WHITE PAPER
VDURA Data Platform V12
Elastic Metadata Engine, snapshots, and SMR optimization: 20%+ throughput, 20x metadata acceleration, 20%+ lower cost per TB.
Download the paper →
ECONOMICS TOOL
Flash Volatility and Storage Economics Optimizer
Model your flash exposure as prices move and see where mixed-fleet tiering changes the cost of a serving fleet.
Run the numbers →
Are you overpaying for inference?
Bring us your serving profile: model sizes, context lengths, concurrency, and how big the corpus is. In one session we will map your footprint hot to cold and show you where the money is going.