GPU cloud providers
Operators with production GPU pools, model serving capability, multi-tenant controls, pricing clarity, and API-ready capacity.
Compute Partners
Connect your GPU capacity to global AI demand.
Aurona brings enterprise AI traffic, agent workloads, model inference, app demand, and private AI routes into approved GPU, bare metal, and regional compute partners.
OpenAI-compatible
Token ledger
GPU route layer
Need
global
Supply
GPU
Model
commit
What partners plug into
Aurona routes AI API, app, model, enterprise, and batch workloads into approved compute lanes, then meters usage through AI Token credits.
Submit capacity for DDDemand
models, agents, apps, enterprise
Supply
bare metal, GPU clusters, racks
Settlement
credits, commits, revenue share
Who should partner
Aurona is designed for serious compute suppliers and GPU capital.
The best partners have real inventory, transparent commercial authority, strong facility and operations evidence, and the ability to expand as AI demand grows.
Bare metal GPU operators
Suppliers of dedicated GPU servers, private clusters, reserved nodes, or rack-scale capacity with clear hardware and SLA details.
AI data center operators
AIDC and data center partners with power, cooling, network, security, remote hands, expansion rights, and regional credibility.
Regional compute providers
Local infrastructure companies that can help Aurona support latency, data residency, enterprise procurement, and regional AI demand.
Sovereign AI infrastructure teams
Organizations building national, sector-specific, or regulated AI capacity where route policy and local control matter.
Cluster-scale GPU investors
Capital allocators and operators with H100, H200, B200, GB200, GB300, MI300-class, or next-generation expansion plans.
Why partners join
Aurona wants large-scale compute nodes in many regions.
For GPU and bare metal suppliers, Aurona should be understood as a demand aggregation platform: we bring model API traffic, AI app usage, enterprise workloads, and token-settled routes to approved capacity partners.
Global regions
multi-region
We are building supply across North America, Europe, Middle East, Japan, Korea, Singapore, India, Latin America, and Australia.
Capacity type
serverless to dedicated
Priority on shared inference pools, dedicated endpoints, 8-GPU bare metal nodes, GB200/GB300-era clusters, reserved racks, and high-throughput capacity.
Edge sites
regional inference
New suppliers are positioning faster site deployment, local token generation, and lower-latency inference as a productized capacity layer.
Commercial model
commitments
Monthly commits, reserved lanes, burst contracts, revenue share, and token-settled provider agreements.
Traffic source
AI API
Aurona converts model, app, enterprise, and agent demand into routed workloads for approved compute partners.
Deployment mode
endpoint-ready
Partners should expose enough runtime, networking, observability, and model-cache detail to become developer-ready API capacity.
Runtime path
scale-to-zero to dedicated
The market is moving from serverless inference for early traffic into reserved endpoints and dedicated clusters as workloads stabilize.
Promotion path
playground to lane
A qualified route should preserve prompt history, cost trace, health checks, and budget approval as it moves from test traffic into production capacity.
Control surface
endpoint API
Capacity partners should expose model inventory, warm-cache status, queue depth, quota, region, and health signals that Aurona can route against.
Marketplace supply
agent runtime
GPU platforms are packaging hosted and self-hosted agent runtimes with deployment state, versioning, monitoring, billing, and usage analytics.
Site activation
weeks, not quarters
New edge inference operators are selling faster regional launches; Aurona should translate that into launch readiness, peering, endpoint packaging, and DD evidence.
Console fit
developer surfaces
Capacity is more valuable when it can appear as dashboard, model hub, playground, endpoint, storage, workflow, and billing surfaces for customers.
Demand sources
Aurona turns AI demand into routed compute workloads.
A compute partner should see Aurona as more than a buyer of servers. The platform can aggregate model API, app, agent, batch, private, and serverless inference demand, decide where traffic should run, and settle the economics through credits and commitments.
Model API traffic
OpenAI-compatible requests, route aliases, fallback traffic, and provider overflow can be moved toward partner GPU capacity when performance and economics fit.
Enterprise private routes
Customers with privacy, data residency, or procurement requirements can be mapped to approved regional hosts, reserved lanes, and private inference stacks.
Serverless inference
Shared GPU pools can back OpenAI-compatible endpoints, short-lived agent bursts, evaluation jobs, and lower-friction developer onboarding.
AI application workloads
Agent platforms, coding tools, RAG products, support automation, media apps, and workflow systems create repeatable inference demand.
Batch and migration jobs
High-volume evaluations, embedding rebuilds, model migrations, offline reasoning, and customer backfills can consume burst or reserved capacity.
Fusion panels
Multi-model panel runs need parallel execution, judge models, private fallbacks, and predictable throughput when production customers scale.
Regional launch demand
New markets need local latency, regional data paths, local supplier relationships, and capacity that can expand as traffic grows.
Batch queue lab · simulated
Plan offline inference before it enters the queue.
Translate text and embedding jobs into request volume, AI Token exposure, and a GPU-backed capacity lane. This lab plans only: it does not submit work, reserve capacity, or quote a real price.
Queue plan
Embedding refresh
12,000 requests · text and embeddings only
Sim. estimate
1.08 AI Tokens
Capacity lane
burst GPU queue
Embeddings shape
/v1/embeddings
Budget gate1.08 / 5.00 AI Tokens
01
validated
02
queued
03
running
04
reconciling
05
completed
Simulated row isolation
One held row does not cancel eligible results.
Completed rows
2
Held for review
1
receipt · batch:embedding-refresh:embeddings:standard:1.08
Capacity promotion · simulated
Promote demand only when the operating evidence agrees.
Compare serverless and reserved capacity with one completed workload window. Aurona keeps native demand, utilization, traffic shape, runtime fit, region evidence, and a normalized AI Token exposure index in the same review packet.
Workload track
Planning outcome
Reserved lane review
sustained eligible demand makes a reserved lane ready for commercial review
Serverless exposureindex 420
Reserved exposureindex 186
Native evidence
420 million tokens / month
Review floor
52% useful utilization
Evidence state
reviewable
Demonstration data only. Exposure indices are normalized planning values, not rates or invoices. This preview does not reserve GPUs, change traffic, promise availability, or debit AI Tokens.
For GPU investors
Why Aurona can matter to compute capital allocation.
GPU investors and infrastructure operators should understand the commercial thesis: Aurona can connect AI demand to regional capacity, qualify supply before traffic, and translate usage into credits, commitments, and partner settlement.
Signal
What Aurona builds
Why it matters to suppliers
Demand aggregation
Model API traffic, enterprise private inference, AI apps, batch workloads, and Fusion panels can all become routed capacity demand.
A partner sees Aurona as a repeat demand channel, not a one-off server buyer.
Regional expansion
Aurona needs capacity close to enterprise data regions, user latency zones, and local AI ecosystems.
Suppliers with multi-region footprints or expansion rights can become more strategic over time.
Commercial clarity
Pilot lanes, monthly reservations, hybrid usage, endpoint commitments, and revenue share are modeled separately.
This helps GPU investors match capital layout to utilization and contract structure.
Technical readiness
Capacity must be endpoint-ready: networking, serving stack, observability, rebuild process, security, and SLA evidence matter.
Better operations increase the chance that capacity can support real customer traffic.
Compliance discipline
Aurona reviews ownership rights, sanctions/export-control exposure, facility evidence, data policy, and customer traffic boundaries.
Trust and DD make the platform more credible to enterprise buyers and long-term partners.
Regional demand
Aurona wants a global compute footprint, not one isolated cluster.
The goal is to place approved capacity close to users, enterprise data regions, model-serving demand, and app traffic. Suppliers with multi-region footprints are especially valuable.
Region
Target markets
Primary demand
Capacity interest
North America
US East, US West, Canada
frontier routing, enterprise apps, private inference
H100/H200, B200/GB200/GB300-ready clusters, L40S, high-memory CPU, dedicated endpoints
Europe
Frankfurt, Amsterdam, London, Paris
EU customer data paths, regulated workloads, low-latency apps
dedicated bare metal, ZDR routes, private networking
Asia Pacific
Tokyo, Seoul, Singapore, Hong Kong
developer traffic, multilingual apps, media routes, regional enterprise
GPU bare metal, fast interconnect, local peering, sovereign route candidates
Middle East
UAE, Saudi Arabia, Qatar
sovereign AI, enterprise pilots, local inference demand
reserved racks, private VLANs, enterprise SLA
India
Mumbai, Chennai, Hyderabad, Bengaluru
high-volume app traffic, support bots, batch automation
cost-efficient GPU clusters, burst capacity, 24/7 ops
Latin America
Brazil, Mexico, Chile
regional latency coverage and local AI app growth
edge GPU nodes, L40S/A100/H100 supply, bandwidth-heavy routes
Capacity bands
We can evaluate small launch lanes and large regional partnerships.
Suppliers do not need to start with a massive global contract. Aurona can assess capacity in practical bands, then expand when route demand, economics, and operations prove out.
Lane
Indicative capacity
Best use
Commercial fit
Evaluation lane
shared route tests
playground history, prompt tests, endpoint smoke tests
usage credits with no capacity commitment
Launch lane
8-32 GPUs
regional pilots, early enterprise routes, app validation
monthly reserved or pilot commit
Production lane
64-512 GPUs
steady API traffic, private inference, batch jobs, support workloads
reserved capacity with usage metering
Strategic region
512+ GPUs
preferred regional supply, multi-customer routing, enterprise procurement
multi-month or annual capacity framework
Burst pool
variable
traffic spikes, provider failover, model launches, offline workloads
usage premium or revenue share
GPU class planning
Capacity review should separate hardware class from runtime promise.
Current inference buyers compare H100, H200, Blackwell, and cost-efficient GPU classes, but Aurona should evaluate them through availability, serving stack, network, region, and upgrade path before attaching traffic.
GPU class
Best fit
Aurona review focus
H100 class
broad production inference and training
baseline availability, price clarity, and mature serving stack
H200 class
large-context and high-concurrency inference
memory bandwidth, KV-cache capacity, and reserved endpoint planning
B200 / GB200 class
frontier throughput and high-density regional lanes
availability date, rack design, networking, and expansion rights
GB300 and next-generation class
future strategic regions and national-scale demand
pre-order evidence, power path, cooling, and commercial authority
L40S / A100 / MI300 class
cost-efficient media, embeddings, smaller models, and batch jobs
workload fit, utilization plan, and fallback economics
Node requirements
The best partners can supply more than raw GPUs.
We are interested in partners who can support production AI inference: reliable hosts, clean networking, fast rebuilds, observability, and commercial terms that can scale with traffic.
Layer
What Aurona needs
Preferred details
Serverless inference
shared GPU pools behind OpenAI-compatible endpoints
autoscaling pools, warm model cache, queue controls, usage metering, cold-start policy
Lifecycle promotion
move from prompt test to serverless, reserved, or dedicated lane
playground history, route trace, policy action, budget approval, endpoint owner
Dedicated endpoints
tenant or route-specific inference endpoints
fixed model stack, private networking, service tier, trace export, failover plan
GPU bare metal
8x GPU servers, dedicated hosts, private clusters
H100, H200, B200/GB200/GB300-ready, L40S, A100, MI300-class capacity
Networking
low-latency east-west traffic and predictable egress
100G/200G/400G options, private VLAN, BGP, cross-connects, clean IP space
Storage
model weights, KV cache, logs, datasets, and checkpoint movement
local NVMe, shared high-throughput storage, snapshot and rebuild support
Operations
production-grade remote hands and failure response
SLA, hardware replacement windows, observability hooks, incident escalation, endpoint rollback
Scaling policy
scale-to-zero, warm pools, queues, and reservation handoff
cold-start targets, batching rules, saturation alerts, dedicated upgrade path
Security
enterprise and regulated AI workloads
private racks, access controls, audit support, data residency alignment
Edge deployment
regional sites close to users and enterprise data
deployment timeline, local peering, operations owner, failover site, and expansion path
Model serving
repeatable runtime and model-library operations
vLLM/TensorRT-LLM, health checks, autoscaling, model swap process, versioned route promotion
Endpoint telemetry
developer-ready capacity and router feedback
queue depth, warm cache, saturation, region, cost class, quota, and retry reason exposed through an API
Console surface
workspace-ready endpoint and workflow controls
dashboard, model hub, playground, endpoint inventory, artifact storage, runtime state, usage, and billing shortcuts
Inference surfaces
Compute becomes product when it is attached to model endpoints.
Aurona should present GPU capacity as developer-ready inference supply: endpoints, model libraries, app workloads, batch jobs, and private lanes that can all be metered through credits.
Surface
What it exposes
Why it matters
OpenAI-compatible endpoint
chat, streaming, tools, structured output
developer migration and app launches
Serverless endpoint
scale-out shared pool, warm cache, queue policy
low-friction onboarding and burst demand
Dedicated endpoint
tenant lane, pinned model, private networking
enterprise capacity and private routes
Edge endpoint
regional site, local peering, failover plan
latency-sensitive customers and data-residency planning
Endpoint health API
queue depth, warm cache, quota, saturation, region
routing decisions and customer-facing capacity status
Model library
available models, runtime version, context, modality
route selection and capacity planning
Workflow runtime
visual pipelines, media assets, reusable nodes
app builders that need GPU-backed execution without managing clusters
Console map
dashboard, model hub, playground, endpoints, storage, workflow, billing
workspace owners need one control room for capacity-backed products
Agent deployment
hosted or self-hosted agent runtime
marketplace apps with versioning, monitoring, and usage analytics
Agent marketplace lane
install, runtime, meter, version, monitor
GPU-backed apps that need deployment without cluster operations
Agent workload
tool calls, retries, long context, memory
serverless bursts or reserved lanes
Batch job
offline evals, embeddings, migration, backfills
spot, burst, or committed capacity
Managed GPU cluster
bare metal, containers, autoscaling, runtime ops
large customers that need control without owning hardware
Private endpoint
tenant lane, region pin, provider key mode
enterprise and regulated workloads
Evaluation criteria
How Aurona qualifies compute supply.
The strongest suppliers help us route production traffic safely. We evaluate inventory, economics, operations, network quality, security posture, and the ability to grow by region.
Signal
What we inspect
Why it matters
Availability
How many nodes are actually ready, reserved, or deliverable within 30/60/90 days.
We prefer suppliers who can show near-term inventory and expansion path.
Unit economics
Monthly node price, power assumptions, bandwidth, support, setup fees, and discount structure.
Clear economics help Aurona convert supply into profitable AI Token routes.
Reliability
Host replacement, remote hands, incident process, hardware burn-in, and SLA commitments.
Production inference needs predictable uptime, not only available GPUs.
Network
Latency, peering, private connectivity, clean IPs, egress terms, and cross-connect options.
Network quality can decide whether a region becomes a high-value route.
Compliance fit
Data residency, physical access controls, logs, SOC posture, and enterprise procurement support.
Enterprise customers often buy the operational controls around the GPU.
Growth path
Ability to add racks, reserve future supply, support new GPU classes, and expand into nearby regions.
Aurona wants partners who can grow with platform demand.
Activation speed
How quickly a site can move from signed packet to reachable endpoint with networking, model cache, monitoring, and support owners.
Regional capacity only matters when it can become routeable without a long custom integration.
Technical integration
Capacity plugs into routing, metering, and operations.
Aurona's compute partner model is designed around a control plane: capacity can be registered, health-checked, routed, metered, and settled without forcing every customer to manage infrastructure directly.
Layer
Aurona object
What it tracks
Control plane
capacity registry
region, GPU type, health, price, policy, and availability
API layer
model endpoints
OpenAI-compatible chat, embeddings, batch, and private route endpoints
Serving layer
model runtime
vLLM, TensorRT-LLM, custom stacks, private endpoints, or partner-operated serving
Traffic layer
Aurona routes
exact model, auto route, Fusion panel, private lane, enterprise policy route
Metering layer
AI Token ledger
tokens, GPU time, route fee, app attribution, customer budget, partner settlement
Operations
observability
latency, errors, saturation, queue depth, host health, incident workflow
Broadcast layer
route event stream
provider selected, fallback, credit debited, capacity saturated, policy blocked
Commercial structures
Several partnership models can work.
Some suppliers want committed monthly revenue. Some want burst utilization. Some want to become a regional AI infrastructure partner. Aurona can evaluate each structure by region, GPU type, price, SLA, and routing demand.
Serverless pool
Partners provide shared GPU pools for developer endpoints, app bursts, evaluation traffic, and overflow from public model providers.
Dedicated endpoint
Partners operate a tenant or route-specific endpoint with pinned model versions, trace export, private networking, and agreed capacity policy.
Reserved capacity
Aurona reserves GPU nodes or racks in a region and routes predictable API traffic into that lane with monthly commercial commitments.
Burst supply
Partners expose approved burst capacity for peak traffic, batch jobs, app launches, and failover from public model providers.
Private inference lane
Enterprise customers can be mapped to dedicated hosts, private networking, zero-retention policy, and approved model stacks.
Revenue share
Aurona can meter customer workloads through AI Token credits and settle compute partner usage from a transparent ledger.
Regional launch partner
Data center and bare metal operators can become the preferred Aurona supply partner for a priority geography.
Migration capacity
Aurona can move high-volume routes from public APIs to partner GPUs when customers need cost control or private deployment.
Economics
The goal is to make compute supply financially legible.
GPU suppliers care about utilization, contract quality, and payment clarity. Aurona's token ledger can make compute economics visible by route, customer, app, region, and partner.
Model
How it works
Why suppliers care
Reserved monthly
Aurona reserves a defined number of nodes or racks for a region.
Suppliers get predictable revenue; Aurona gets route certainty.
Usage premium
Capacity is consumed when traffic requires extra throughput or provider failover.
Useful for burst pools and regions where demand is still ramping.
Hybrid commit
A smaller monthly base plus usage upside above the committed capacity.
Balances supplier stability with Aurona traffic growth.
Endpoint commit
A dedicated endpoint is reserved for a model, route, or tenant with a minimum capacity floor.
Useful when enterprise buyers require predictable latency and operational review.
Token settlement
Partner usage can be represented as line items in the AI Token ledger.
Helps connect model, app, enterprise, route, and compute economics.
Regional partner
A supplier becomes preferred supply in a target geography.
Best for operators with data center presence and ability to expand.
Partner path
From capacity profile to routed production traffic.
Aurona should make suppliers feel that there is a practical path from an available GPU inventory sheet to real AI API demand.
01
Capacity profile
Share region, GPU type, node count, network, storage, pricing, SLA, and availability date.
02
Technical review
Validate host access, provisioning process, model serving fit, observability, and security boundaries.
03
Endpoint packaging
Define serverless, dedicated, edge, batch, workflow, or agent-runtime surfaces with health and billing fields.
04
Console mapping
Map capacity into dashboard, model hub, playground, endpoint, storage, usage, and billing shortcuts.
05
Commercial lane
Choose reserved capacity, burst, hybrid, revenue share, or region partner structure.
06
Pilot traffic
Run test inference workloads, benchmark latency, throughput, stability, and route economics.
07
Production routing
Attach approved capacity to Aurona routes, token ledger, monitoring, and partner settlement.
Supplier capacity intake
Submit GPU, bare metal, and data center supply for due diligence review.
Aurona can only route production workloads to capacity that passes due diligence (DD). Suppliers should provide order-form level detail, verifiable evidence, commercial terms, and explicit compliance attestations before any pilot, reservation, or customer traffic.
Truth
Every claim must be supported by documents, inventory evidence, or facility proof.
Compliance
Sanctions, export controls, ownership rights, and customer data rules are reviewed before traffic.
Safety
Physical security, access controls, incident response, and isolation are mandatory DD gates.
Supplier identity
Legal entity, accountable person, and supplier type for first-pass DD.
Facility and region
Where the capacity sits, what standard the facility meets, and what evidence is available.
GPU inventory and expansion
Card class, current availability, and realistic scale-up path.
Server, storage, and runtime
Detailed hardware configuration, storage topology, and serving stack readiness.
SLA and operations
Operational credibility, replacement windows, remote hands, and escalation process.
Power, cooling, and facility economics
Power envelope, cooling method, energy price, and rack density.
Network and commercial offer
Connectivity, egress, IP quality, contract shape, and pricing basis.
DD evidence and special notes
Documents and facts Aurona can inspect before any production traffic or commitment.
DD attestations
These declarations are required before Aurona can evaluate production traffic, reserved capacity, customer data paths, or payment commitments.
Contact compute supply
Have available GPUs, bare metal, racks, or regional capacity?
Start with the structured intake above so our team can review DD, security, facility evidence, commercial terms, and technical fit. After the packet is ready, Aurona can evaluate whether that supply fits AI API routing, enterprise, and app workloads.
Before we route traffic
Truthful inventory, ownership, and commercial authority
Facility proof, data center standards, and security controls
GPU model, server configuration, storage, and expansion path
Power, cooling, network, egress, clean IPs, and SLA terms
Pricing, contract term, evidence packet, and DD attestations
Aurona.ai
Build on the token network for AI applications, models, and compute.
API
OpenAI-compatible
Billing
AI Token credits
Capacity
GPU-backed routes