See how much AMD Instinct™ Coder can save you

All figures are illustrative. Your actual costs will vary with electricity rates, facility efficiency, hardware utilization, model mix, and other operational factors.

1. Baseline scenario: "Without Launchpad" assumes 100% of AI workloads run against frontier model APIs (Anthropic, OpenAI, or equivalent). Your input is that current monthly bill. Year 1 = monthly spend × 12; each later year multiplies the prior year by (1 + annual growth). "With Launchpad" for each year factors in the annual subscription cost of Spectro Cloud Inference Launchpad, hardware depreciation (across a 3-yr timeframe), the operating costs, and the annual baseline spend on frontier models.
2. Minimum monthly spend and default: This calculator is built for teams running AI at enterprise scale, where dedicated local inference starts to earn its keep. $35K is a realistic mid-market threshold; it's a modeling floor, not an industry benchmark. The $100K default reflects an enterprise AI team at scale: above Ramp's 90th percentile of business AI spend at $73,030 per month, below the $140,842 monthly average. For context, Gartner reports that organizations spent an average of $1.9 million on GenAI initiatives in 2024. See Gartner: Hype Cycle for Artificial Intelligence and Ramp: How Much Do AI Tokens Cost Businesses? (avg $140,842/mo; 90th percentile $73,030; 95th $211,409; 99th $831,338).
3. Growth default (30% YoY): conservative against Gartner's forecast of 47% worldwide AI spending growth for 2026. See Gartner: Worldwide AI Spending to Grow 47% in 2026.
4. Hardware pricing: $450,000, based on the Supermicro AS-8126GS-TNMR 8U GPU SuperServer, configured with 8× AMD Instinct™ MI325X GPUs. AMD Instinct™ Coder assumes new hardware at list price with no discount; this is the baseline estimate before depreciation.
5. Hardware depreciation: standard straight-line depreciation with no salvage value. Annual hardware cost = list price ÷ depreciation years. At the default 3 years that's $450,000 ÷ 3 = $150,000 per year. Only years inside the depreciation window carry a hardware charge, so a 1- or 2-year depreciation period leaves later years with no hardware line.
6. Operating costs ($50,000/yr per node): applied to the new hardware as an ongoing charge, spread evenly across the 12 months. Split in the breakdown table: approximately $11,000/yr for power and cooling, plus $39,000/yr for rack, IT ops, network, and other datacenter overhead. Power and cooling ($11,000/yr): at 85% realistic utilization, the node uses about 79,854 kWh of electricity per year. After accounting for typical data center cooling and facility overhead using a 1.54 PUE, total annual energy use rises to about 122,976 kWh. At the U.S. industrial average electricity rate of $0.0862/kWh, that works out to approximately $10,600 per year, rounded to $11,000. Formula: 79,854 kWh × 1.54 PUE × $0.0862/kWh = ~$10,600/year, rounded to $11,000. Actual costs will vary by region, electricity rates, utilization, facility efficiency, and cooling model. Sources: EIA Electric Power Annual, Table 2.7; Uptime Institute Global Data Center Survey 2025.
7. Launchpad subscription: $50,000 per server per year, billed annually up front. Treated as a Year 1 upfront cost with renewals at the start of Years 2 and 3.
8. Cost-effectiveness of on-prem inference: a Dell / ESG independent analysis found on-premises inference 2.9× to 4.1× more cost-effective than API-based service for sustained workloads. See ESG: Understanding the Total Cost of Inferencing LLMs on-premises with Dell (PDF).
9. Developer-usage tiers (Light $100, Typical $550, Heavy/agentic $1,000 per developer per month): illustrative modeling points inside the $100 to $1,000+ range Gartner reports for AI coding and agentic token costs. Gartner sets the range; the three tiers here are our anchors for translating your monthly spend into approximate developer counts. Estimated developers per tier = monthly spend ÷ that tier's per-developer cost, rounded to the nearest whole developer. See Gartner: How to Plan for Escalating AI Token Costs. Ramp's 2026 transaction data (Ramp: How Much Do AI Tokens Cost Businesses?) adds supporting evidence at the organization level.
10. Sunk-cost hardware path: when GPUs are already available in house, hardware CapEx = $0. Operating costs still apply as an ongoing charge.