Sell GPUs to a retail CEO

# Executive Proposal: Accelerating Walmart’s AI with NVIDIA GPUs ## 1) Discovery — Candidate Workloads and Value Assumption: Focus on 4 near‑term, high‑value, GPU‑amenable workloads with clear P&L impact and measurable SLAs. - Demand forecasting (merchandising/supply chain) - Data: POS, inventory, promotions, weather/events, price, supplier lead times. - Pain: High MAPE for long‑tail SKUs; slow retraining; batch windows delay replenishment. - GPU fit: Deep learning (temporal fusion, transformers) trains/infer 10–30× faster on GPUs; supports higher‑granularity models. - Value drivers: Lower stockouts/overstocks; fresher forecasts; reduced safety stock. - Route optimization (middle/last mile) - Data: Orders, stops, time windows, capacities, traffic, driver constraints. - Pain: Solvers time out; cannot re‑optimize intraday; miles and overtime creep. - GPU fit: Parallelized heuristics/metaheuristics/OR on GPUs solve 10–100× faster; enables frequent re‑optimization. - Value drivers: Fewer miles, better OTIF, lower fuel/driver cost, higher asset utilization. - Computer vision at edge (stores/DCs) - Data: Cameras at self‑checkout, aisles, docks; planograms; POS events. - Pain: Shrink at SCO; manual audits; lagging compliance checks. - GPU fit: Real‑time detection/classification/segmentation; multi‑stream inference on a single edge GPU. - Value drivers: Shrink reduction, labor savings, improved on‑shelf availability, safety. - LLM assistants (associates, merchants, care) - Data: SOPs, product catalogs, tickets, policies, merchant notes; retrieval over internal docs. - Pain: Long handle times, knowledge fragmentation, training ramp. - GPU fit: High‑throughput, low‑latency inference for RAG, summarization, structured extraction. - Value drivers: Lower AHT, higher CSAT, faster decision cycles for merchants. ## 2) ROI and TCO — Quick Model (GPU vs CPU) Guiding formulas: - TCO_annual = (CapEx / Useful life years) + Power_cost + Cooling/space + SW/Ops + Cloud (if any) - Power_cost = kW_draw × PUE × 8,760 × $/kWh - Payback (months) = Initial_investment / Monthly_net_benefit Illustrative platform assumptions (state clearly as assumptions; adjust with vendor quotes/site data): - CPU node: $12k, 0.5 kW at load - GPU node: $250k, 8 kW at load - Electricity: $0.10/kWh; PUE 1.3; Useful life: 3 years - Utilization (sustained): CPU 30%; GPU 60% (better consolidation/queuing) - Speedups vs CPU (typical ranges): forecasting train/infer 10–30×; route optimization 10–100×; vision infer 5–30×; LLM infer throughput 10–40× Consolidated throughput sizing example (illustrative): - To hit SLAs across the four workloads, a CPU‑only fleet requires ~400 nodes; a GPU fleet achieves equivalent/greater throughput with ~20 nodes (20× consolidation). CapEx (3‑year straight‑line): - CPU: 400 × $12k = $4.8M → $1.6M/yr - GPU: 20 × $250k = $5.0M → $1.67M/yr Power OpEx: - CPU: 400 × 0.5 kW = 200 kW → 200 × 1.3 × 8,760 × $0.10 ≈ $228k/yr - GPU: 20 × 8 kW = 160 kW → 160 × 1.3 × 8,760 × $0.10 ≈ $182k/yr Other OpEx deltas (illustrative): - Space/racks: fewer racks for GPUs; save ~$50–100k/yr - Admin/ops: fewer nodes; save 0.5–1 FTE (~$75–150k/yr) - Software licensing: solver/DB per‑node licenses drop with consolidation (varies) Key takeaway: Even if CapEx is similar, GPUs deliver far higher effective throughput at lower power per unit work, and—more importantly—enable business outcomes via latency/accuracy that CPUs cannot meet. Latency‑to‑revenue pathways (numeric examples): - Forecasting: Assume $1B in annual pilot category sales; reduce stockouts by 1 percentage point via fresher/accurate forecasts → recovered sales ~$10M; at 25% gross margin → ~$2.5M annual gross profit. - Route optimization: 500 daily routes; $250/day route cost (fuel+driver). 1% miles reduction from intraday re‑optimization → ~$456k/year savings. - Computer vision: Pilot 50 stores with combined shrink losses of $20M; reduce SCO‑related shrink by 10% → ~$2M/year savings. - LLM assistant (care): 10k contacts/day; AHT 6 min; labor $0.60/min. 30s reduction → $3k/day → ~$1.1M/year; plus CSAT uplift. Illustrative payback for a 90‑day pilot stack (2 GPU nodes on‑prem + edge kits): - Investment: $1.2M (hardware lease/dep’n, integration, MLOps, change mgmt) - Annualized benefits from pilots above (conservative 50% realization during pilot ramp): - Forecasting: $1.25M - Routing: $0.23M - CV: $1.0M - LLM: $0.55M - Total ≈ $3.03M/year → ~$0.76M/quarter - Payback ≈ $1.2M / $0.76M ≈ 1.6 quarters (~5 months) Sensitivity/guardrails: - Stress test with 50% lower benefits or 25% higher costs; payback still < 12 months. - Validate speedups with quick benchmarks on a thin slice of real data. ## 3) 90‑Day Phased Pilot and Success Criteria Scope: One region (50 stores), one DC, and one e‑commerce/care workload. - Phase 0 (Week 0–2): - Finalize scope, data access, security review, stores/DC selection - Stand up secure GPU environment (on‑prem or VPC), edge kits staged - Baseline metrics captured (MAPE, miles, shrink, AHT/CSAT) - Phase 1 (Week 3–6): Build & integrate - Forecasting: Train GPU models; integrate to replenishment sandbox; shadow‑mode - Routing: GPU solver integrated; A/B vs current plans - CV: Deploy edge inference in 50 stores; alerting tied to POS/events - LLM: RAG assistant for associates/care; guardrails, observability - Phase 2 (Week 7–10): Run & optimize - Turn on controlled interventions (e.g., re‑optimize 2×/day; actionable CV alerts) - Tune thresholds; scale to target load; reliability and latency SLOs - Phase 3 (Week 11–12): Measure & decide - Financial readout; TCO vs CPU baseline; ESG metrics (kWh/throughput) - Scale plan, commercial terms, and change‑management package Success metrics and acceptance thresholds (scale‑up if all green): - Forecasting: MAPE improvement ≥ 10%; in‑stock +1 pp; safety stock −5%; payback < 12 months - Routing: Solve time ≤ 10 min for regional batch; miles −2% (min −1%); OTIF +1 pp - CV: SCO shrink −10% in pilot stores; false positives < 1 per 100 transactions; infer latency < 50 ms per stream - LLM: AHT −20%; CSAT +3 pts; hallucination < 1% on audited prompts; PII leakage 0 incidents - Platform SLOs: 99.9% service uptime; P95 latency within SLA for each workload ## 4) Stakeholders and Risks Stakeholders: - Executive: CEO (sponsor), CFO (ROI), CIO/CTO (platform), Chief Merchandising, Chief Supply Chain, SVP Stores, CISO/Chief Privacy, Chief Sustainability, GC/Legal, Procurement/Vendor Mgmt - Ops: Store Ops, DC Ops, Transportation, Contact Center, Data Platform/ML Ops, Loss Prevention, Network/Edge Engineering Key risks & mitigations: - Edge deployment complexity - Mitigate: Ruggedized edge GPUs; offline‑first design; OTA fleet mgmt; store‑friendly install; network QoS - Vendor lock‑in - Mitigate: Open standards (ONNX, containers, Kubernetes), portable models, abstraction layers; multi‑cloud/hybrid reference arch - Data security & privacy (PII/PCI, CV in stores) - Mitigate: On‑prem inference for sensitive workloads; encryption at rest/in transit; RBAC; DLP; privacy‑by‑design (no face ID), DPIAs - ESG/power - Mitigate: Measure perf‑per‑watt; consolidate CPU fleets into fewer GPU nodes; schedule for off‑peak; decommission obsolete hardware; renewable sourcing - Change management & skills - Mitigate: Training for engineers/ops; co‑delivery with partners; playbooks and SRE runbooks; success‑based scaling ## 5) Objections and Data‑Backed Rebuttals 1) “GPUs are too expensive.” - Data: For equivalent throughput, illustrative sizing shows ~20× fewer GPU nodes than CPU (e.g., 20 GPU vs 400 CPU). Annual power drops (~$228k → ~$182k), admin/rack costs drop, and—most importantly—business benefits from latency/accuracy (e.g., $2.5M from stockout reduction on $1B category) dwarf modest CapEx differences. Payback in ~5–12 months under conservative assumptions. 2) “We can just use the cloud/CPUs we already have.” - Data: GPU speedups (10–40×) allow intraday re‑optimization and real‑time vision that CPUs/cloud VMs at current quotas/latencies often cannot meet without overprovisioning. Consolidation also cuts per‑unit software licensing and egress. Hybrid keeps sensitive data on‑prem (zero egress) while bursting spiky LLM loads. 3) “We don’t have the skills to run GPU AI at scale.” - Data: Pilot limits scope to 50 stores + 1 DC + 1 service, with MLOps/SRE guardrails. Consolidating from hundreds of CPU nodes to tens of GPU nodes reduces fleet complexity. Training + co‑delivery + reference architectures cut time‑to‑competency; measurable outcomes in 90 days de‑risk scale‑up. ## 6) Clear Ask and 90‑Day Timeline Ask: - Approve a 90‑day pilot with budget up to $1.2M (hardware lease/dep’n, integration, edge kits, change mgmt) - Designate executive sponsors (CFO, CIO/CTO) and operational owners (Merchandising, Supply Chain, Stores, Care) - Grant data access and store/DC selection for pilot; green‑light privacy/security reviews and limited edge installs - Success‑based scale decision at Day 90 tied to acceptance thresholds above Timeline (90 days): - Days 0–14: Scope, security/data access, environment build, baseline - Days 15–42: Model/solver/LLM/CV build and integrations; edge staging - Days 43–70: Controlled rollout, tuning, reliability hardening, KPI tracking - Days 71–90: Financial/ESG readout, TCO vs CPU, scale plan and exec decision Validation plan: - Run thin‑slice benchmarks to confirm speedups on real data - Independent finance partner to validate benefit tracking and payback - Red/amber/green gates at Day 30/60/90 aligned to acceptance thresholds Conclusion: Consolidating on GPUs unlocks capabilities (real‑time, higher‑granularity models) that translate directly into revenue lift and cost savings. With conservative assumptions, the pilot pays back in months and creates a scalable platform for multi‑year ROI.