QRF Class-Independent Infrastructure Core

QRF is a class-independent infrastructure Core for massive, dynamic computation. The same protected Core is demonstrated across Routing, AI Resource Allocation, and Storage Placement & Replication without creating a new domain-specific engine per class. It is designed for integration across decision, control, orchestration, and execution layers while keeping the Core independent of any single domain or operational class. Published Routing evidence reaches 500M nodes / ~3.0B directed edges on a 6-core / 12-thread desktop-class PC with 16 GB RAM, using resource-adaptive operation beyond resident memory, with no GPU/HPC and no mandatory preprocessing. The three published classes are representative engineering proofs of the class-independent architecture, not its boundary. The 500M-node / ~3.0B-edge Routing result is separate scale-and-hardware evidence, demonstrating operation on a standard 6-core / 12-thread system with 16 GB RAM and no GPU/HPC. It was used as a large-scale resource-pressure and fault-tolerance test, not as a claimed architectural memory ceiling or maximum supported scale; production fault-tolerance guarantees require separate validation.

One Core·No New Domain Engine per Class·Standard Hardware at Massive Scale

Discuss Evidence & Integration View Engineering Evidence

Engineering Validation: Gates 1–3 are PASS under frozen benchmark conditions. Gate 4 is PASS for the tested service workload across Routing, AI Resource and Storage, including updates under load and state correctness.

3 benchmarked classes = architecture proof · 500M / ~3.0B = scale & hardware proof · 55 canonical measured runs · Patent Pending

500M / ~3.0B
Routing scale · hardware proof · resource-adaptive

0.8095 ms p95
AI Resource at 100M · median Core-post p95

0.8923 ms p95
Storage at 100M · median Core-post p95

6-core / 12-thread
16 GB RAM · no GPU/HPC

Why QRF

QRF changes the unit of infrastructure implementation. One reusable Core can support distinct operational classes without a separate domain-specific engine for each one, while published evidence demonstrates massive-scale operation on standard desktop-class hardware without GPU/HPC. Routing, AI Resource Allocation, and Storage Placement & Replication are proof points for the architecture — not three separate products.

500Mnodes tested in Routing

~3.0Bdirected edges

< 1 msAI & Storage Core-post p95 at 100M in the measured suite; excludes upstream work and API overhead

16 GBRAM benchmark system

How the Operating Model Changes

Common Domain-Specific Pattern	QRF Operating Model
Domain-specific implementation	Reusable class-independent Core
New class requires new domain engine	New operational class without a new domain-specific engine
Single system-layer role	Core usable across system layers
Fixed use case	Extensible operational classes
Resident-memory-centric execution	Resident + resource-adaptive operation
Preprocessing-dependent architecture	No mandatory preprocessing
State-size-coupled execution pressure	Resource-aware execution at massive scale

Selected capability comparisons. Supporting evidence and measurement boundaries are available for technical review.

Massive Scale

QRF Routing evidence spans from 1M to 500M tested nodes and reaches approximately 3.0B directed edges. Across the published range, QRF demonstrates practical Core execution as graph size increases, including resource-adaptive operation beyond resident-memory scale.

100× scale (1M → 100M): Routing ~4.09× median-p95 growth

Low-Latency Core Execution

At the tested 100M scale, AI Resource Allocation and Storage Placement & Replication remain below 1 ms median Core-post p95 across five measured runs. Full API/service-path latency is evaluated separately.

AI ~1.66× · Storage ~1.78× median Core-post p95 growth across 100× scale

Standard Hardware

Published engineering benchmarks were produced on a 6-core / 12-thread general-purpose desktop with 16 GB RAM. At 200M and 500M Routing scale, resource-adaptive execution is used as the dataset exceeds available resident memory; no GPU or HPC platform is required for the published evidence.

6-core / 12-thread · 16 GB RAM · no GPU/HPC

Class-Independent Core

Routing, AI Resource, and Storage keep their own workload and result semantics while preserving the same Core architecture.

One Core · No New Domain Engine per Class

Separate Evidence Streams

Each operational class has its own workload, result type, latency, throughput, and validation evidence. This produces independently interpretable evidence for Routing, AI Resource Allocation, and Storage while preserving the same class-independent Core model.

Separate workloads · Separate results · vendor-controlled same-Core binary attestation

Engineering Evidence → Technical Due Diligence

QRF enters technical discussions with a defined engineering evidence base. Organizations can review the architecture, benchmark evidence, and measurement boundaries against their own infrastructure requirements; existing results and supporting records are available for verification during technical due diligence.

Evidence Review → Due Diligence → Integration

Global Infrastructure CAPEX — The QRF Opportunity

QRF has recorded large-scale Core execution on commodity CPU hardware: 500 million routing nodes, approximately 3 billion directed edges, and three demonstrated classes. This chart translates that hardware-efficiency opportunity into physical datacenter capital-cost scenarios. The $1,000 reference is the modeled purchase price of the QRF benchmark machine; the company-scale figures are defined facility-size scenarios, not audited company expenditures.

PHYSICAL DATACENTER CAPEX · 100% BASELINE COMPARISON · NO CLOUD RENTAL

QRF reference Core machine

$1,000

Amazon AWS — modeled site

$10M

Microsoft Azure — modeled site

$25M

Google Cloud — modeled site

$50M

Meta AI — modeled site

$100M

OpenAI / partner compute — modeled site

$250M

Oracle OCI — modeled site

$500M

NVIDIA ecosystem — modeled site

$1,000M

Nonlinear scale makes the $1,000 QRF reference visible beside million-dollar physical infrastructure baselines. Company names identify technology ecosystems; the displayed CAPEX values are scenario inputs.

Measured QRF scope: The shared Core has benchmark evidence in Routing, AI Resource Allocation, and Storage Placement & Replication. The architecture also targets Control, Orchestration and Execution integration. The economic calculation applies to infrastructure serving replaceable operations; it does not count unrelated GPU training, raw storage capacity, or physical network links.

View calculation basis, 25% / 50% / 75% / 100% cases and global deployment formula

Per-site gross avoided future capital: eligible physical CAPEX × selected replacement percentage. Net benefit: avoidable future CAPEX + realizable resale + attributable operating savings − production QRF hardware, integration, migration and exit costs. Past sunk investment is not newly saved.

Illustrative ecosystem / site	Modeled CAPEX	25% ceiling	50% ceiling	75% ceiling	100% ceiling
Amazon AWS — modeled site	$10M	$2.50M	$5.00M	$7.50M	$10M
Microsoft Azure — modeled site	$25M	$6.25M	$12.50M	$18.75M	$25M
Google Cloud — modeled site	$50M	$12.50M	$25.00M	$37.50M	$50M
Meta AI — modeled site	$100M	$25.00M	$50.00M	$75.00M	$100M
OpenAI / partner compute — modeled site	$250M	$62.50M	$125.00M	$187.50M	$250M
Oracle OCI — modeled site	$500M	$125.00M	$250.00M	$375.00M	$500M
NVIDIA ecosystem — modeled site	$1,000M	$250.00M	$500.00M	$750.00M	$1,000M

Illustrative $100M facility cost build-up: compute/accelerators $65M + memory/storage $12M + networking $8M + power/cooling $10M + allocated facility $5M = $100M. This is a transparent modeled equipment budget, not an invoice from the named companies. Apply the replacement percentage only to eligible assets.

Global expansion: one $100M site × 25% = $25M; 10 independent sites = $250M; 100 = $2.5B; 1,000 = $25B in gross future capital avoidance. At 50%, these become $50M / $500M / $5B / $50B; at the theoretical 100% ceiling, $100M / $1B / $10B / $100B. Count unique physical assets once; hyperscalers and their tenants may share infrastructure.

Download the calculation PDF (physical CAPEX model)

Calculation basis: Recorded QRF benchmark performance is presented separately from modeled datacenter investment. The $1,000 Core-machine purchase assumption and $10M–$1B facility scenarios are explicit model inputs. The 100% column shows the full modeled infrastructure baseline; 25%, 50% and 75% show proportional modeled capital exposure. Realized savings depend on the eligible hardware replaced and the cost of the deployed QRF system.

The QRF Difference

One reusable Core. Multiple operational classes. Massive scale on standard hardware. Routing, AI Resource, and Storage are proof points; the Core is the asset.

Application Domains

QRF is not bound to one domain or one system layer. The class-independent Core is designed for integration across large, dynamic system environments and across decision, control, orchestration, and execution layers. The examples below illustrate potential environments; published benchmark evidence remains separately identified in the Technical Evidence sections.

MOBILITY

V2X & Connected Mobility

Dynamic vehicle, roadside, and mobility-network systems with changing topology, connectivity, state, and operational constraints.

V2XVANETConnected vehicle

CONNECTED INFRASTRUCTURE

IoT, Telecom & Edge

Large connected-device, gateway, telecom, and edge environments with continuously changing state, resources, relationships, and execution requirements.

IoT / IIoTTelecomEdge

GRAPH-BASED SYSTEMS

Dynamic Infrastructure Systems

Large graph-representable systems whose state, relationships, constraints, and operational actions change over time.

Large graphsDynamic stateInfrastructure Core

Evidence coverage: Published performance figures cover Routing, AI Resource Allocation, and Storage Placement & Replication. These are the current public evidence streams for the class-independent Core; they do not define the architectural boundary of QRF.

Beyond the Current Benchmarked Classes

QRF separates the protected Core from class identity. Routing, AI Resource Allocation, and Storage Placement & Replication provide three published evidence streams; they demonstrate that the same architecture is not hard-coded to one domain. Additional expressible classes can be introduced through the class contract without redefining QRF as a new domain-specific engine.

One Core Above Individual Classes and Layers

QRF is designed for integration across decision, control, orchestration, and execution environments. Current public benchmarks document three operational classes. Further integrations are discussed against the target requirements, with confidential implementation details reserved for an appropriately protected technical review.

Decision — Demonstrated UseControl — Architectural UseOrchestration — Architectural UseExecution — Architectural UseClass-Independent Core

Global Web Service & Deployment Architecture

QRF extends the protected class-independent Core through an operational service architecture designed for controlled integration and deployment. The same service model supports Single-Host and Distributed topologies across SaaS, Private Cloud, On-Premise, and Edge environments.

Single-Host & Distributed

Self-contained deployments and distributed infrastructure use the same QRF service model, allowing deployment topology to change without redefining the Core architecture.

Operational Control Plane

Centralized management covers companies, environments, class types, graph versions, licenses, credentials, limits, and operational resources.

Protected Execution Layer

Controlled API access supports authorized use of QRF with defined access policies and operational limits. Proprietary implementation details are reserved for confidential technical review.

Operations & Governance

Queue and execution visibility, monitoring, roles and access control, audit logs, incidents, backups, and platform-service management provide the surrounding operational controls.

Pilot-stage integration: Following evidence verification and technical due diligence, a jointly agreed pilot supports interface alignment, deployment coordination, and integration into the target environment. Its scope builds on existing engineering evidence and agreed access policies.

Class 01 — Routing Decision

Routing Decision is QRF Class 01. Its benchmark evidence is measured independently, while AI Resource and Storage maintain their own class-specific KPIs and evidence streams.

Result Snapshot

Maximum tested scale500M nodesLargest published Routing scale, demonstrating operation on a graph containing approximately 3.0B directed edges. This test is not a memory ceiling; it also exercised large-scale resource pressure and fault-tolerance behavior, without establishing production HA.

Median p958.8663 msMedian Routing engine-query p95 across 5 stable measured runs at the 500M-node scale; the Hybrid result is conditioned fixed-hotset steady-state, not cold/unseen-pair or full API latency.

Measured benchmark runs25Five stable measured runs at each of five Routing scales.

HardwareStandard PCThe same standard desktop-class benchmark context is disclosed publicly to make the scale-to-hardware relationship clear.

QRF Public Technical Brief A concise overview of QRF positioning, benchmark proof points, application areas and evidence review pathway.

Download Public Technical Brief (PDF) Verify published PDF downloads (SHA-256)

Canonical Evidence Set — 2026-10-02

55 measured benchmark runs are represented on this page: Routing 25, AI Resource 15, and Storage 15. A vendor-controlled same-binary attestation links all three classes to the same frozen Core binary. This is engineering evidence, not third-party certification.

QRF Technical Demo

100M nodes • 600M edges • 10 live routing queries • 10/10 valid

Watch QRF execute and validate routing queries on a 100M-node / 600M-edge graph.

Controlled technical demonstration at 100M nodes / 600M edges. Production KPI and SLA qualification are established separately for the target deployment environment.

Routing Benchmark Results

Scale	Edges	Scale Class	Memory Mode	Median p95	Measured Runs
1M	6M	Standard-scale	Memory-resident	0.2452 ms	5/5
10M	60M	Standard-scale	Memory-resident	0.4478 ms	5/5
100M	600M	Standard-scale	Memory-resident	1.0036 ms	5/5
200M	1.2B	Large-scale	Memory-adaptive	3.4782 ms	5/5
500M	3.0B	Large-scale	Memory-adaptive	8.8663 ms	5/5

Measurement Scope

The figures above report median p95 across five stable measured runs per scale. At 200M and 500M, Routing uses Hybrid conditioned fixed-hotset steady-state engine-query scope; these figures are not cold/unseen-pair latency or full API/service-path latency. Existing results are reviewed within their stated measurement scope. Target-environment service requirements and deployment-specific SLAs are addressed separately during integration planning.

Class 01 Routing — Core p95: 1M-100M

Median p95 across 5 stable measured runs per scale

Median p95 reaches 1.0036 ms at 100M tested nodes.

Class 01 Routing — Core p95: 200M-500M

Median p95 across 5 stable measured runs per scale

Benchmark version note: The current 500M headline reports 8.8663 ms median p95 for five fixed-hotset steady-state Hybrid runs. The previously published 6.997 ms figure came from an earlier benchmark series; do not treat the two as the same measurement without matching run conditions.

Median p95 is 8.8663 ms at 500M tested nodes / approximately 3.0B edges, under conditioned fixed-hotset steady-state Hybrid scope.

Routing Engineering Evidence

Published results span 1M to 500M tested nodes, approximately 6M to 3.0B edges, and 25 stable measured runs.

Class 02 — AI Resource Allocation

AI Resource Allocation is QRF Class 02. It evaluates AI-infrastructure resource decisions and returns dedicated class-specific allocation results with its own benchmark evidence.

Result Snapshot

Scale100MLargest currently published AI Resource Allocation benchmark scale using the frozen workload.

Frozen workload1000 jobs/runThe same 1000-job workload is used at every tested scale to improve comparability between 1M, 10M, and 100M.

Median p950.8095 msMedian Core-post p95 across 5 stable measured runs at 100M.

Median throughput2,398 jobs/sMedian decision throughput across 5 stable measured runs at 100M.

Benchmark scope1M / 10M / 100M · 1000 frozen jobs/run · 5 measured runs/scale · benchmark suite

Benchmark Results

Scale	Workload	Median mean / request	Median p95	Median throughput	Measured Runs
1M	1000 frozen jobs/run	0.246 ms	0.4870 ms	4,014 jobs/s	5/5
10M	1000 frozen jobs/run	0.327 ms	0.5907 ms	3,022 jobs/s	5/5
100M	1000 frozen jobs/run	0.415 ms	0.8095 ms	2,398 jobs/s	5/5

AI Resource latency and throughput are reported under measured Core execution (excluding upstream processing and Python overhead) scope on the frozen workload. One clean conditioning observation per scale is retained as NOT_MEASURED; five stable measured runs are then collected per scale. Full API/service-path metrics are evaluated separately.

Class 02 AI Resource — Core-post p95

Median Core-post p95 across 5 measured runs per scale

At 100M scale, median Core-post p95 is 0.8095 ms.

Class Evidence

Class-specific workload: The same frozen 1000-job workload is used at each tested scale.

Class-specific result: The public result is a dedicated AI Resource Allocation decision result.

Low-latency evidence: Five stable measured runs are collected at each scale after a non-measured clean conditioning observation.

Class 03 — Storage Placement & Replication

Storage Placement & Replication is QRF Class 03. It evaluates distributed-storage placement and replication decisions and returns dedicated class-specific storage results with its own benchmark evidence.

Result Snapshot

Scale100MLargest currently published Storage Placement & Replication benchmark scale using the frozen workload.

Frozen workload1000 requests/runThe same 1000-request workload is used at every tested scale to support scale-to-scale comparison.

Median p950.8923 msMedian Core-post p95 across 5 stable measured runs at 100M.

Median throughput2,539 req/sMedian decision throughput across 5 stable measured runs at 100M.

Benchmark scope1M / 10M / 100M · 1000 frozen requests/run · 5 measured runs/scale · benchmark suite

Benchmark Results

Scale	Workload	Median mean / request	Median p95	Median throughput	Measured Runs
1M	1000 frozen requests/run	0.227 ms	0.5027 ms	4,270 req/s	5/5
10M	1000 frozen requests/run	0.312 ms	0.6675 ms	3,161 req/s	5/5
100M	1000 frozen requests/run	0.389 ms	0.8923 ms	2,539 req/s	5/5

Storage latency and throughput are reported under measured Core execution (excluding upstream processing and Python overhead) scope on the frozen workload. One clean conditioning observation per scale is retained as NOT_MEASURED; five stable measured runs are then collected per scale. Full API/service-path metrics are evaluated separately. Placement success and decision quality are documented within the class-specific evidence scope and are available for verification during technical due diligence.

Class 03 Storage — Core-post p95

Median Core-post p95 across 5 measured runs per scale

At 100M scale, median Core-post p95 is 0.8923 ms.

Class Evidence

Class-specific workload: The same frozen 1000-request Storage workload is used at each tested scale.

Class-specific result: The public result is a dedicated Storage Placement & Replication decision result.

Class 03 identity: Storage is measured as an independent operational class with its own benchmark section and evidence stream.

ENGINEERING VALIDATION

Engineering Gates

Internal engineering validation confirms the tested behavior across the four gates.

Gate	Validated behavior	Status
Gate 1	Correctness and exact-oracle checks	✓ PASS
Gate 2	Resource behavior and operating modes	✓ PASS
Gate 3	Performance and concurrency	✓ PASS
Gate 4	Updates under load and state correctness	✓ PASS

These are internal engineering test results for the defined test scopes.

Evidence Verification & Integration

Verify QRF’s existing engineering evidence and assess its fit with your infrastructure. Engage directly with the QRF Project Lead to review documented results, measurement scope, architecture, and integration requirements.

Discuss Evidence & Integration Download Public Technical Brief

Evidence Review & Integration Path

Verify existing engineering evidence, establish technical fit, and coordinate integration. Any agreed pilot focuses on environment alignment and deployment interfaces.

Review Existing EvidenceArchitecture, benchmark results, and engineering-gate records

Verify Results & ScopeRecorded outputs, evidence integrity, and measurement boundaries

Assess Technical FitInfrastructure requirements and documented Core capabilities

Agree Integration ScopeInterfaces, topology, responsibilities, and any jointly agreed pilot

Coordinate IntegrationEnvironment alignment, operational coordination, and deployment

Shahram Darvishi

QRF Project Lead · Software Architect & AI Researcher

✉ shahram.darvishi@qrf-ai.com

Direct technical contact for QRF architecture review, technical due diligence, strategic integration, and protected commercial discussions.

Measurement Scope & Hardware

Published benchmark figures use the current canonical evidence set: 25 Routing measurements plus 15 AI Resource and 15 Storage measurements. AI Resource and Storage use Core-post scope that excludes upstream processing and Python overhead; 200M/500M Routing uses conditioned fixed-hotset steady-state Hybrid engine-query scope. Full API/service-path KPIs, cold/unseen-pair latency, and deployment-specific SLAs are characterized separately for the target environment.

Benchmark hardware: 6-core / 12-thread general-purpose desktop with 16 GB RAM. No GPU acceleration or specialized server/HPC hardware was used.