The Infaio Agentic Grid
Agents do more than generate answers. They use tools, revisit information, and work through a task one step at a time.
Scaling agents takes more than adding GPUs. It takes an architecture that makes every step work smarter. The Infaio Agentic Grid brings specialized compute closer to where agents work, with our intelligent GridRunner orchestrator matching each step to the right model and processor, and our GridVault data sovereignty layer designed to protect company data and intellectual property. Connected through a distributed network of modular compute sites, the grid is built for a clear payoff: faster completions, lower costs, and capacity that can grow wherever agents are needed.
ATOMs at the Metro EdgeAgentic Task Optimized Module. 1 MW modular data centers, installed on existing power and connected as a nationwide mesh network.↗
What is the Grid?
The Infaio Agentic Grid is an infrastructure architecture, not a single processor or a routing service layered over someone else’s cloud.
Its physical building block is the Agentic Task Optimized Module, or ATOM: a modular compute site designed to bring heterogeneous systems closer to users and their enterprise data.
GridRunner coordinates workloads across the grid. GridVault supplies the sovereignty layer. Infaio optimizes the whole system, from power and silicon to the completion of an agent’s work.
Optimize for a completed agentic workload.
Not one component in isolation.
GridRunner · Intelligent orchestration
Not every step needs the same model or processor. GridRunner is designed to route each step based on its cost, speed, and data requirements, while reusing available context instead of starting from scratch.
Choose the right resource. Match each step to suitable models and processors.
Reuse what is already known. Keep usable context available between turns.
Keep related work close. Integrate inference, agent execution, and tools.
Prefill is the work of processing a model’s input before it generates a response. Infaio’s design uses GPUs for compute-intensive prefill and batch workloads that suit their parallel processing strengths.
Keep high-throughput compute focused on the stages that need it.
Decode is the continuing work of generating a response. Specialized processors are selected and tuned for efficient token delivery, with the aim of reducing energy per token while keeping agents responsive.
Optimize throughput, responsiveness, and energy per token.
Sandboxes, tool calls, runtimes, and retrieval need execution capacity alongside inference. Infaio integrates dense CPU compute into the fabric rather than treating these as a distant, separate layer.
Bring the work around the model closer to the model.
Workload placement, processor tuning, local execution, and infrastructure cost are designed together.4
GridRunner performance
Every agent run involves a sequence of model calls. Explore how GridRunner is designed to reduce the time and cost of that inference work, then see the difference across the full workflow.
GridRunner · Inference replay
Compare model-call time and inference cost, excluding tool execution.
Press play to follow both timelines.
Illustrative replay.6
Explore the annual economicsTime spent calling models, before tool execution.
less per replay.
The annual scenarios include the same trace-derived tool allowance on both paths. Expand the timing breakdown without replaying the inference comparison.2
Estimated workflow time, including tools
Your baseline volume of comparable workflow runs.
Enter a whole number from 0 to 1,000,000,000.
OpenRouter
Infaio
One example workload, with two optimization priorities. The same annual workflow volume on both paths.
OpenRouter
Infaio
Modeled completions with the same annual execution time and concurrency.
Time saved / run
Workforce hours returned
At $100/hour, with 100% of saved time usable by the workforce.
Productivity value, not automatic payroll savings.
Enter an hourly cost from $0 to $1,000.
100% assumes one person is waiting on every run and can use all the time saved. Lower this for unattended runs, overlapping waits, or time that cannot be put to other work.
Metro Edge · Proximity by design
Every repeated trip between an agent, its tools, and its data adds waiting. Infaio’s Metro Edge architecture places compute inside metropolitan areas, close to users and their data, to shorten those paths while keeping company data within its required boundaries.
Permitted working context, retained inside the boundary.
External APIs and customer-retained tools remain external.
Agent productivity depends on more than how fast a model responds. Infaio brings inference, agent execution, and supported tools closer together, with an architecture designed to turn shorter waits into faster progress.
For always-on agents
Time spent waiting between steps adds up across repeated runs. Shorter handoffs can help agents complete more work within the same operating window.
For people working with agents
An answer is more valuable when it arrives while someone is ready to act. Faster handoffs help reduce interruptions across research, analysis, coding, and decision-making.
Sovereignty & GridVault
Protect where company data and working context are held. Keep the freedom to choose models and agent frameworks as requirements change.
Placement and execution governed by policy.
Physically sovereign ATOMs establish a controlled infrastructure boundary for where data, models, and agent workloads operate.
Hardware-enclave isolation and cryptographic controls are designed to protect live context, KV caches, agent memory, and tool-execution sandboxes.
Keep control of the data and freedom across the ecosystem. Open frameworks and model choice are designed to avoid a proprietary lock-in; workload placement remains governed by sovereignty policy.
ATOMs & existing power
Capacity is only useful when it can be brought online. Infaio’s deployment model starts with suitable sites that already have power, connectivity, and proximity to demand.
Infaio has identified over 500 MW of capacity directly and through REIT and energy partners.4
GPU inference and CPU execution for work that values throughput over immediate response.
High-throughput GPU capacity and CPU execution for sustained, demanding workloads.
GPU prefill, specialized decode, and CPU execution for responsive agent interactions.
Target unused capacity at existing powered sites instead of making every deployment depend on new generation or transmission.4
Factory-integrated, modular ATOMs are designed to simplify deployment and bring capacity online in increments. Standardization supports a more predictable build and operating cadence.
A connected grid creates options for maintenance, recovery, and alternative workload placement.4
Start with reserved GPU or RDU capacity for the models and software you run.
Explore capacityAccess model serving on workload-matched compute without managing each processor yourself.
Discuss your needsBring agents, harnesses, tools, and context into an orchestrated, policy-governed system.
Join the BetaCapacity reservations
Explore AMD GPUs and SambaNova inference processors on the Infaio Agentic Grid. Tell us what you need, when you need it, and where it needs to run.
Plan reserved GPU capacity around your AI workloads and long-term growth.
$1.95/ GPU-hour9
Long-term contractsPlan your next generation of AI capacity around the AMD Helios rack-scale architecture.
$7.95/ GPU-hour9
Long-term contracts
Ultra-fast, interactive inference, targeting 2–10× the tokens per second of GPU-only serving for more responsive agents.4
$8.95/ chip-hour9
Long-term contractsStarting rates apply to long-term contracts with non-refundable reservation fees. Subject to contract terms and conditions.
Planning a dedicated 50–500 MW grid? Explore Partner Grids ↗Build with GridRunner
Bring your agents.
Help shape what comes next.
Lower costs. Faster completions. More control over your data and context. Tell us what would make the biggest difference for your agents, and register your interest in the GridRunner beta.
For teams exploring, building, or running agents. Share your priorities so we can start the right conversation.
Joining the interest list does not guarantee beta access. Timing and availability are subject to change.
Interest submitted
Your beta interest has been submitted to Infaio. We’ll use your priorities to follow up about participation. Beta access is not guaranteed.
Partner Grids
Reserve the capacity to build at your scale. Infaio will develop private grids for partners building the next generation of AI applications.
Shape the compute mix, locations, and sovereignty requirements around your business, with a phased capacity plan built around your growth.5
50–500MW
Dedicated capacity.Plan an inference grid around model releases and agent adoption. Align capacity, processor choices, and deployment locations with your roadmap.
Reserve capacity as your products grow. Bring inference, tools, and agent execution together to support responsive applications and better economics.
Place detection, investigation, and response agents closer to enterprise data, with sovereignty requirements built into where workloads run.
Plan private capacity for financial institutions, healthcare, and other privacy-sensitive industries. Align deployment locations and data controls with applicable privacy laws and jurisdictional requirements.
Plan nearby compute for manufacturing, distribution, and robotics. Bring AI workloads closer to machines, sensor data, and operational systems, with capacity shaped around each site.
Support autonomous vehicle and drone programs with regional compute for fleet coordination, simulation, and data processing. Complement onboard systems with capacity near operating areas.
The value of earlier capacity
A private grid can begin producing value as capacity comes online in phases. Explore how deployment timing changes the revenue opportunity from the same planned capacity.
Cumulative modeled inference revenue (USD)
Infaio Private Grid
Conventional AI Data Center
Default benchmark: $159.5M per utility MW/year, from SemiAnalysis’s Vera Rubin example. The benchmark already includes 60% utilization. Both paths use the same rate; Infaio adds 15 MW/month from month 4.
| Month | Infaio MW | Conventional AI Data Center MW | Infaio-path revenue | Conventional-path revenue |
|---|
Tell us your target capacity, preferred regions, and deployment timeline.
Your Partner Grid inquiry has been submitted to Infaio. We’ll follow up at the email address you provided.
The team
Experience in computing platforms, distributed systems, and large-scale data center operations. Company-building, engineering, and operational depth to bring the grid into service.
CEO & Founder
Former executive at Broadcom, AltaVista, and Compaq, with experience building and leading multi-billion-dollar businesses.
President & COO, Co-founder
Former startup CEO and venture capitalist who led global notebook PC businesses at Dell, HP, and Compaq.
Chief Platform Officer, Co-founder
Former Dell senior vice president across orchestration, security, management, and networking software; founding VP of Engineering at Panasas.
VP, Grid Operations
Former CyrusOne SVP of Data Center Operations, with experience deploying and managing 1.2 GW across 50 sites.
VP, Software and Grid Architecture
Former Micron and Dell vice president with expertise in storage, memory, high-performance computing, and distributed systems.
Infaio
We are developing The Infaio Agentic Grid with a systems-first view of agentic AI: software, silicon, sovereignty, and deployment engineered together.
Based in Austin, Texas, our focus is the architecture and the work of bringing it into operation. We welcome conversations with technology partners, site and power partners, investors, and engineers.
Your inquiry has been submitted to Infaio. We’ll follow up at the email address you provided.