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How to Build a Quantum-Ready Procurement RFP for AI Infrastructure

UUnknown

2026-02-16

10 min read

A procurement guide and editable RFP template balancing AI chips, soaring memory costs, and optional quantum services for 2026-ready infrastructure.

Build a quantum-ready RFP for AI infrastructure — without blowing your budget on memory

Hook: As AI models grow, procurement teams face two brutal realities in 2026: AI accelerators are eating up chip supply and memory prices have surged, while interest in quantum services has moved from academic curiosity to a potential strategic advantage. This guide gives IT procurement teams a practical, vendor-ready RFP template and a checklist that balances core needs (AI chips, memory costs, networking) with optional quantum integrations — so you can get demonstrable AI performance today while keeping your roadmap open for quantum-enabled workflows.

Executive summary — what to ask for first

Start procurement with one principle: separate the baseline AI infrastructure your business needs now from optional quantum services you may want to gate into later. Your RFP should require vendors to quote both a firm baseline (compute, memory, storage, network, software) and a modular add-on for QaaS, SDK connectors, hybrid orchestration. That dual-track structure protects budget predictability while giving you an auditable path to experiment with qubit-backed services.

Why this matters in 2026

Two 2025–2026 trends reshape procurement:

Memory strain: High-bandwidth memory (HBM) and server DRAM have become hotter commodities as AI accelerators proliferate—raising unit prices and complicating capacity planning. Recent market reporting from early 2026 highlights memory price pressures driven by AI demand, making cost-per-GB a first-class procurement metric.
Quantum moves toward practicality: Cloud providers and service vendors now offer expanded quantum access models—ranging from simulators and emulators to real NISQ devices and early fault-tolerant prototypes. That means procurement can contract for optional quantum services today, but must clearly define availability, metrics, and integration expectations.

"Memory chip scarcity is driving up prices for laptops and PCs" — reported coverage at CES 2026 (Forbes, Jan 2026).

Procurement strategy — two tracks, one contract

Structure the RFP as two interlocking modules:

Baseline AI Infrastructure (mandatory): GPU/AI accelerators, memory and storage, network fabrics, virtualization, orchestration, security, and managed service levels.
Quantum-Ready Add-ons (optional): QaaS access, hybrid orchestration connectors, SDK compatibility, and defined POC engagements for quantum algorithms.

This separation lets you evaluate apples-to-apples costs for immediate AI needs while comparing optional quantum offers only on technical merit and business value.

RFP Template — section-by-section

Below is a compact but complete RFP layout you can paste into your procurement system. Replace bracketed sections with your specifics.

1. Executive overview and business objectives

{
  "project_name": "AI Infrastructure & Quantum-Ready Platform",
  "business_objectives": [
    "Train and serve LLM-family models up to X billion parameters",
    "Lower training cost per token by Y% within 12 months",
    "Pilot hybrid quantum-classical algorithms for target use-case Z"
  ]
}

2. Mandatory baseline technical specifications

Compute: Minimum number of AI accelerators (model + count), interconnect topology (NVLink/IB), required PCIe lanes.
Memory: Per-accelerator HBM sizing and server DRAM (GB), memory bandwidth (GB/s), and allowed memory scaling options (expansion slots, poolable memory).
Storage: Required NVMe capacity, throughput (GB/s), IOPS, and tiering policy for hot/warm/cold data.
Network: East-west bandwidth (e.g., 100/200/400Gbps), RDMA support, and topology diagrams.
Software stack: OS images, container runtime, orchestration (Kubernetes, Kubeflow/Ray), ML frameworks and versions, driver compatibility, and automated deployment scripts.
Security & compliance: Data residency, encryption at rest/in transit, key management, and compliance standards (SOC2, ISO27001, GDPR). For automated checks in CI/CD and compliance workflows reference guidance on legal & compliance automation for model pipelines.

3. Performance and acceptance tests (mandatory)

Require vendors to run standardized benchmarks and a small set of buyer-specified workloads:

MLPerf Training and Inference (where applicable).
Custom end-to-end pipeline: dataset ingest, training, checkpointing, inference latency at QPS targets.
Memory stress and OOM scenarios: profile peak DRAM and HBM usage and per-step memory allocations.
Cost per training step and cost per inference—built from measured resource use and vendor pricing.

4. Pricing model (mandatory)

Ask vendors to split pricing into predictable components:

Capital/Consumption for baseline compute and storage.
Memory surcharge: show per-GB cost and escalation formula tied to market indices.
Network and interconnect charges (if separate).
Optional quantum access pricing: per-shot, per-hour, subscription, and priority-access tiers.

5. Optional quantum services & integrations (optional module)

Ask vendors to explicitly state what they provide and how it integrates:

Access models: simulator, emulator, cloud QPU, dedicated remote QPU reservations.
Qubit technology and quality metrics: qubit count, coherence times, gate fidelities, readout error rates, and calibration cadence.
SDKs & tooling: supported SDKs (Qiskit, Cirq, PennyLane, Amazon Braket SDK), Python/REST APIs, and example notebooks demonstrating hybrid workflows. For examples of CLI and developer tooling reviews, consider vendor tool evaluations when assessing SDK quality.
Hybrid orchestration: connectors for Kubernetes, Ray, or custom job managers; integration patterns for parameter-server or data-parallel hybrid jobs.
SLAs for quantum services: average queue wait, max queue wait, calibration windows, and scheduled maintenance notices.

6. Support, SLAs, and escalation

Uptime SLAs for core infrastructure (99.9% / 99.95% etc.).
Mean time to respond (MTTR) for severity levels.
Dedicated on-boarding and training deliverables for hybrid quantum-classical operations.

7. Trial, POC, and acceptance criteria

Define a 60–90 day trial window with objective metrics:

Performance targets from acceptance tests.
Integration milestones for CI/CD and model deployment pipelines. Include automated compliance and CI checks referenced earlier for operational governance.
For quantum add-ons: a POC that executes a defined hybrid workload end-to-end and produces reproducible results.

8. Exit & migration

Data export format, timelines, and any charges.
Infrastructure portability: container images, IaC, Helm charts, and build artifacts.

Scoring rubric and evaluation checklist

Use a weighted scoring model to make trade-offs visible. Example weights (customize to your priorities):

Technical fit (40%) — compute, memory, network, software compatibility.
Total cost of ownership (20%) — CAPEX/OPEX, memory surcharge, support costs.
Performance & benchmarks (15%) — MLPerf/custom pipeline metrics.
Quantum integration readiness (10%) — SDKs, API quality, POC plan.
Security & compliance (10%) — certifications, encryption, governance.
Vendor viability & support (5%) — customer references, financial health.

Example evaluation checklist items:

Does the vendor provide per-accelerator HBM specs and DRAM per host?
Can the vendor commit to per-GB memory pricing with a market-indexed escalation clause?
Does the vendor publish measured quantum device metrics and calibration schedules?
Is there a working hybrid job orchestration demo (Kubernetes + QaaS connector)?
Are acceptance benchmarks reproducible in our environment?

Practical guidance: benchmarking memory costs and capacity planning

Memory is now a first-class budget item. Here are concrete steps to quantify memory impact:

Profile representative training and inference jobs locally. Capture peak DRAM and accelerator HBM usage per step.
Calculate effective memory-per-parameter for model family: memory_needed = peak_HBM + server_DRAM_resident.
Compute memory cost per training step: cost_mem_step = (HBM_cost_per_device * HBM_fraction_used + DRAM_cost_per_GB * DRAM_used) / steps_per_hour.
Ask vendors to provide an itemized memory surcharge and a market-linked escalation clause (e.g., tied to a DRAM index or published cost index, with annual caps). Consider architectural options like short-term leasing or auto-sharding to mitigate spikes.

Simple formula example:

Cost_per_training_hour = (num_accelerators * accelerator_hour_price) + (total_DRAM_GB * DRAM_price_per_GB_hour) + storage_io_costs + network_costs

Hybrid architecture and integration patterns

Design hybrid pipelines so quantum components are modular and replaceable. Common patterns in 2026:

Preprocessing-in-classical → quantum subroutine → classical post-processing: Good for VQE-like or variational workflows where quantum circuits act on reduced subproblems.
Model-in-the-loop: Classical model training where gradient estimation or small combinatorial subroutines are offloaded to QPU or simulator.
Orchestration: Use Kubernetes operators, Ray actors, or custom queue workers to call QaaS REST endpoints or SDK methods. Require vendors to provide example manifests and CI/CD playbooks.

SLA specifics for quantum services — what to insist on

Quantum hardware has unique slowness and variability. In your RFP, request:

Queue time metrics (average and 95th percentile) and guaranteed priority access windows for paid tiers.
Calibration schedule notices and historical uptime of QPUs.
Device health metrics as part of job responses (gate fidelity, error bars) and SDK hooks to retrieve them.
Data retention and export guarantees for measurement results and raw pulses (if provided).

Sample contract clauses (copy-and-paste friendly)

Two short examples you can adapt:

Memory Price Escalation Clause:
Vendor will provide per-GB DRAM and per-device HBM pricing for the initial 12-month term. Annual price adjustments may not exceed X% and must be tied to a publicly published DRAM pricing index (e.g., [name index]). Any adjustment requires 90 days written notice.

Quantum POC Acceptance:
Vendor shall provide access to either a QPU or hardware-accelerated simulator for a 60-day POC. Acceptance requires repeatable execution of the buyer-provided hybrid workload, documented end-to-end runbooks, and delivery of result artifacts. Failure to meet acceptance metrics allows buyer to terminate optional quantum services without penalty.

Procurement timeline and stakeholders

Recommended timeline for enterprise procurement:

Week 0–2: Finalize RFP scope and stakeholder sign-off (IT, ML Ops, Security, Finance).
Week 3–6: Issue RFP and collect proposals.
Week 7–10: Shortlist and run vendor-run benchmarks + onsite/remote tests.
Week 11–14: POC (60–90 days) with acceptance criteria.
Week 15+: Contract negotiation and onboarding.

Stakeholders to include: procurement lead, ML engineering lead, infrastructure architect, security/compliance officer, finance controller, and a quantum subject-matter advisor (internal or external).

Hypothetical example: mid-market SaaS provider

Scenario: A SaaS vendor trains a 10B-parameter model quarterly and wants a quantum pilot for a combinatorial optimization feature. Using the RFP above they:

Bid out baseline AI infrastructure focused on HBM-rich accelerators to reduce inter-node sharding.
Negotiated a capped memory escalation clause of 8% per year with transparent indexation.
Selected a vendor offering an optional QaaS subscription with documented SDK connectors and a 60-day POC to run a hybrid subroutine. The POC required a 95th-percentile queue time below 6 hours for priority access.

Result: predictable per-quarter training costs and a funded path to test quantum value without long-term quantum commitments.

Advanced strategies and 2026 predictions

Look ahead and bake flexibility into contracts:

Memory trading: Expect spot markets and leasing for HBM-equipped cards in 2026–2027. Contracts that permit short-term leasing at predefined rates can reduce cost spikes.
Quantum partnerships: In 2026 many cloud providers expanded partner marketplaces offering hybrid toolchains — demanding explicit interoperability requirements in RFPs pays off.
Vendor diversification: Relying on a single vendor for both accelerators and memory can be risky. Consider split procurement (compute from one vendor, memory pools from another) where feasible.

Actionable takeaways — checklist for the RFP final pass

Split the RFP into mandatory baseline and optional quantum modules.
Make memory pricing explicit and require a market-indexed escalation clause.
Demand reproducible benchmarks and an objective POC acceptance plan.
Require SDK compatibility and example hybrid orchestration manifests for quantum integrations.
Insist on SLAs that cover quantum-specific metrics: queue times, calibration windows, and device health telemetry.

Closing — next steps

Procurement in 2026 is about balancing immediate AI throughput while keeping an upgrade path to quantum capabilities. Use the RFP template and checklist above to solicit clear, comparable proposals that separate the science project (quantum experimentation) from the production budget (AI training and serving). Good procurement makes strategic optionality affordable.

Call to action: Download the full editable RFP template and scoring spreadsheet from Flowqubit or schedule a 30-minute workshop with our quantum-ready infrastructure advisors to tailor the RFP to your environment.

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