Capacity
Model weights, KV cache, context, concurrency and memory reserves.
AI & digital infrastructure
Data Center AGI Inference
Connect AI data center assessment, GPU inference feasibility and solution architecture. Review capacity, power, cost and evidence gates with AssetSolve.
More useful inference. A defensible infrastructure decision.
The operating flow
One connected path through the work.
- 01
Define the workload
Freeze model, precision, context, concurrency, quality floor and latency targets. Record the source and age of every material input.
- 02
Assess the constraints
Evaluate GPU memory, cache state, bandwidth, compute, utilization, power and facility limits. Keep measured observations distinct from estimates and operator attestations.
- 03
Test feasibility
Compare future-state demand, quality, latency, power and budget constraints with the current evidence. Unknown inputs remain evidence gates; policy applicability and migration schedules are explicit.
- 04
Architect and validate
Produce a conditional architecture, traceable decisions and phased acceptance plan. Agree a bounded customer pilot and require qualified approval and verified recovery before production changes.
Data Center AGI Inference / engineering scope
From the next workload to the next megawatt.
A GPU purchase, a serving-engine change and a cooling investment share one question: how much accepted work will this infrastructure deliver? AssetSolve brings the workload, resource constraints and business case into one reviewable decision.
- 01 / WorkloadQuality · context · concurrency
- 02 / InfrastructureGPU · state · network · facility
- 03 / Value caseAccepted work · energy · cost
- 04 / Delivery gateEvidence · human review · recovery
Full-stack coverage
Ten channels. One connected assessment.
Open a channel to see the engineering questions and the deliverable. Applicability depends on the workload, platform and evidence available.
01Facility & cooling
Relate IT load to facility overhead, cooling assumptions and the site power envelope. Review liquid- and warm-water-cooling scenarios with qualified facility engineers.
Review output: Facility-boundary energy scenario and constraints register.
02Server, rack & network
Account for host, network and other IT overhead alongside accelerator draw. Preserve separate boundaries for device, server, rack and facility measurements.
Review output: An explicit resource and metering boundary.
03Power management
Assess idle draw, clocks and power-cap scenarios against throughput, latency and recovery requirements. Average energy use and provisioned peak capacity answer different questions.
Review output: Power/performance scenario with approval and recovery prerequisites.
04Utilization & demand
Evaluate pooling, reserves, headroom, forecast scaling and deferrable demand. Keep queueing and tail-latency targets visible when increasing utilization.
Review output: Demand, headroom and service-target sensitivity analysis.
05Inference serving
Evaluate continuous batching, chunked prefill, quantization, speculative decoding and prefill/decode separation where workload and engine support permit.
Review output: Workload-specific candidate levers and quality/latency tests.
06Placement & topology
Compare model and expert placement, cache locality, communication cost and hardware constraints before choosing a distributed serving layout.
Review output: Placement assumptions, dependencies and bottleneck analysis.
07Memory, context & state
Size model weights and KV cache against context and concurrency. Examine prefix reuse, cache tiering, compaction lineage and state traffic rather than memory capacity alone.
Review output: Capacity and state plan with documented reserves.
08Workload & tokens
Define useful output and token budgets. Count retries, rejected responses and review work; compare alternatives only at an equivalent acceptance boundary.
Review output: Frozen workload manifest and accepted-output definition.
09Hardware & fleet
Use capacity, roofline and fleet models to examine memory fit, bandwidth and compute ceilings, device counts and provisioned power. Validate analytical bounds against customer measurements.
Review output: Hardware-fit and fleet-capacity scenarios.
10Economics, carbon & grid
Connect resource use to electricity prices, carbon factors, capital recovery and grid constraints. Keep assumption-driven forecasts separate from realized financial benefits.
Review output: Transparent investment case with sensitivities and exclusions.
Inside the delivered toolkit
An engineering baseline you can inspect.
6calculators
210assessment checks
57remediation templates
33efficiency levers
The combined registry separates 136 operator attestations, 40 calculator checks, 23 composite checks and 11 metric checks. The nine added quantum/post-quantum checks are operator attestations; they do not scan an enterprise cryptographic estate. Missing evidence stays visible; a completed checklist is distinct from a measured production outcome.
State
Resident state, attention/cache architectures, storage and traffic.
Roofline
Bandwidth, compute and arithmetic-intensity limits on prefill and decode.
Fleet
Device counts, resident workload state and provisioned power.
Efficiency
Energy, cost, carbon and useful throughput within a defined boundary.
Quantum planning
Conditional resource, facility and access-economics scenarios with separate workload and verification gates.
Reproducibility before rollout
The reviewed combined toolkit passed 244 tests and all 22 validation stages in each of 6 Windows/Linux jobs across Python 3.10, 3.12 and 3.14. Its evidence set includes 268 source records, the reproduced inference reference and a reviewed quantum-planning reference. These checks validate toolkit behavior; they do not establish customer fleet efficiency, production serving integration or savings.
Assessment, plan and ledger views support read-only review. The workbench adds stateless feasibility previews and JSON/Markdown design exports. Sequenced recommendations retain applicability, dependencies and conflicts. All four added quantum/post-quantum remediation templates remain manual.
Integration & delivery
Connect evidence before changing operations.
Evidence intake
Connector code accepts manifests, OpenMetrics, engine arguments, accelerator observations and Kubernetes inventory, including mappings for vLLM, SGLang and DCGM. Live permissions, platform versions, metric semantics and workload coverage are verified within the customer engagement.
Serving changes, GPU controls and cluster mutations require a separately authorized implementation and tested recovery path. Hosted review does not execute production changes.
Enterprise delivery
Bridge the infrastructure value case to investment planning, procurement, asset maintenance and finance through an agreed system-of-record contract. SAP, Oracle, BTP and Joule integration can be scoped as part of the wider enterprise program; it is not a native connector claim for this toolkit.
Free initial assessment
Define the operating question, workload owner and evidence access.
Scoped diagnostic
Deliver the baseline, missing-evidence register, scenarios and prioritized plan.
Bounded pilot
Compare equivalent quality and latency targets; test failure and recovery.
Accepted implementation
Agree customer integration, operational ownership, rollout and support.
Assessment → feasibility → solution architecture
Take the evidence into a decision package.
Future-state requirements and design
Bring task demand, quality, latency, per-replica evidence, capital and operating budgets, power and explicit target dates into the feasibility review. Blank measurements remain unknown. The output identifies blocked gates, missing evidence, conditional decisions and an owned acceptance plan.
Explore Infrastructure Feasibility & Solution Architecture ↗Post-quantum readiness
Review inventory evidence, confidentiality lifetimes, supplier dependencies, policy scope and migration schedules alongside infrastructure choices. The workbench produces a plan for target-system verification; it does not install algorithms, scan live cryptographic systems or authorize a migration.
Explore Post-Quantum Readiness ↗The sixth calculator and nine quantum scenarios support separate research questions about resources, facilities, access economics and migration timing. A quantum roadmap or typed input contributes no inference-capacity or energy-saving credit. Production capacity and cryptographic migration require evidence from the target systems.
The complete operating context
The facility and the AI workload belong in the same conversation.
Reliability & critical assets ↗
Connect electrical and cooling asset findings to engineering review and approved maintenance.
Inspection & integrity ↗
Retain controlled inspection subjects, readings, failures and accepted corrective evidence.
Maintenance & outage readiness ↗
Coordinate equipment, parts, engineering, work execution and quality handover.
Safe work & controlled change ↗
Connect permits, isolation, work eligibility and reinstatement with qualified site authority.
Capacity & inference value ↗
Explore workload quality, context, state, recovery and cost per verified task.
Finance & investment decisions ↗
Align accepted operational evidence with investment, cost and financial reconciliation.
Engineering reference points
Make the measurement method explicit.
Independent documentation helps define benchmark scenarios, telemetry and resource boundaries. References describe the underlying technologies; they do not imply certification, partnership or endorsement of AssetSolve.
The business case
Define the improvement before the investment.
The operating challenge
Infrastructure decisions separate workload quality, GPU capacity, facility constraints and financial assumptions, leaving teams without a common baseline.
The decision to improve
Connect six engineering/planning calculators, a 210-check assessment and 57 remediation templates to future-state requirements, feasibility gates and a conditional solution architecture.
Who should be in the room
- Data center operator
- AI platform leader
- Infrastructure and capacity architect
- Facilities engineering leader
- FinOps and investment team
A focused first scope
One workload and infrastructure boundary: freeze the input manifest, review evidence coverage, compare resource/cost constraints and produce a conditional design with owned pilot acceptance criteria. Initial assessment is free.
Build a measurable baseline.
Cost per accepted task = total in-scope workload and facility cost ÷ tasks meeting the agreed quality and service targets. Compare equivalent workloads; validate scenario estimates against customer measurements.
Agree the baseline, comparable scope, adoption, review effort, and realization assumptions. Include delivery and operating costs; count each benefit once.
Application scope
The work it brings together.
- 210-check combined infrastructure and readiness assessment
- Six calculators: capacity, state, roofline, fleet, efficiency and quantum planning
- 57 remediation templates with explicit manual actions
- 33 conditional efficiency levers across ten channels
- Future-state requirements and feasibility gates
- Conditional architecture and JSON/Markdown design exports
- Scoped post-quantum evidence and migration-plan review
- Reproducible context packs and engineering handover
People & systems
Clear roles. Clear ownership.
Data center operators · AI platform engineers · Infrastructure architects · Capacity and facilities planners · FinOps and investment teams · Enterprise risk reviewers
Authoritative records
- Customer-authorized workload and configuration manifests
- Accelerator and serving telemetry
- Cluster inventory and facility energy records
- Approved engineering and financial baselines
Governed by design
Authority stays with the people who own it.
The hosted toolkit review is read-only. Operational execution is a separate, customer-authorized activity with explicit scope and recovery criteria.
AGI describes the forward-looking infrastructure problem space. The delivered toolkit provides deterministic engineering assessment and planning.
Customer production telemetry, serving-engine integration, quality impact and realized savings must be established in the engagement. Toolkit validation does not establish fleet performance.
Facility controls, production cluster changes and ERP postings remain with the customer’s authorized systems and people.
Read the workflow contract ↗A practical first scope
Start with a bounded operating decision.
Start with a free initial assessment. Scope one workload and infrastructure boundary, then define future-state requirements, evidence coverage, capacity/cost scenarios, feasibility gates and a conditional architecture. Add a post-quantum inventory and migration-plan review where relevant. Paid delivery follows a written proposal with pilot acceptance criteria.
Measures to agree with your team
Explore indicative investment ↗Questions, answered
The details that shape your next step.
What does Data Center AGI Inference deliver?
A connected assessment, feasibility review and conditional solution architecture for AI infrastructure. The scope covers GPU sizing, state/cache, utilization, energy, cooling, economics, evidence gaps and an acceptance plan. Customer integration and production outcomes require target-system validation.
Can the assessment produce a solution design?
The workbench connects current findings with declared future-state requirements and exports a conditional architecture, decisions, evidence gaps and phased acceptance plan in JSON or Markdown. Design generation does not authorize infrastructure changes.
How does post-quantum readiness fit the offering?
A scoped workstream reviews cryptographic inventory evidence, applicable policy assumptions, supplier dependencies and migration schedules. Quantum scenarios remain planning models. The reviewed toolkit has not scanned a customer cryptographic fleet, tested a live TLS endpoint or performed a production migration.
Does this mean the toolkit is an AGI system?
No. AGI is the research and infrastructure-planning context. The current product is a deterministic assessment and planning toolkit, with reproducible calculators and explicit evidence requirements.
Which integrations are available?
The toolkit includes evidence connectors for manifests, OpenMetrics, engine arguments, accelerators and Kubernetes, with mappings for vLLM, SGLang and DCGM. Each customer’s live access, versions, mappings and outputs require validation. SAP, Oracle, BTP and Joule business integration is separately scoped.
How are efficiency gains established?
Compare an agreed baseline and candidate using the same workload, quality threshold, latency target and accounting boundary. Include rejected outputs, retries, review effort and facility overhead. Scenario estimates are planning outputs; customer savings require measured results.
How does it connect to data center reliability?
Combine the inference capacity and efficiency assessment with AssetSolve’s asset reliability, inspection, maintenance, safe-work and controlled-change offerings. Facility authorization and operational safety remain with qualified site authorities.
How do we start, and what does it cost?
The initial assessment is free. The proposal defines the diagnostic, integrations, bounded pilot, acceptance evidence, implementation and support terms. Indicative enterprise engagement ranges are published on the pricing page.
Connected products
Applications that carry the work forward.
AI & digital infrastructure
Post-Quantum Readiness
Review cryptographic inventory evidence, data confidentiality lifetimes, platform dependencies and migration schedules. Build a scoped post-quantum readiness and crypto-agility plan with explicit interoperability, performance and recovery gates.
AI & digital infrastructure
Infrastructure Feasibility & Solution Architecture
Connect AI infrastructure and post-quantum readiness assessments to future-state requirements, evidence gates and a conditional solution architecture. Review capacity, quality, latency, cost, resource and migration constraints before committing to implementation.
Asset operations
MRO Nexus
An equipment-centered maintenance and overhaul workflow spanning commercial authorization, engineering, induction, planning, supply, execution, quality, and close.
Work with AssetSolve
Start with the decision you need to improve.
Your initial assessment is on us. A first conversation about your challenge, use-case fit, and the right next step.