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 $50K default reflects a typical enterprise AI team, sitting just below Ramp's 90th percentile of business AI spend at $73,030 per month. 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. These figures apply to the On-prem deployment option.
6. Hosted deployment (owned Supermicro H14 at Vultr): assumes you own a Supermicro H14 server with 8× AMD Instinct™ MI325X GPUs, purchased for approximately $212,000 (roughly: 8× MI325X $120,000; dual AMD EPYC 9005 CPUs $18,000; 3 TB DDR5 $33,600; 8× 4 TB NVMe $5,600; chassis and integration $35,000) and hosted at Vultr. The server purchase is CapEx; everything else is an operating cost. Depreciation is fixed at three years: $212,000 ÷ 3 = $70,666.67 per year. Power and cooling assumes air cooling, a 1.5 PUE, and electricity at $0.12/kWh: $1.57/hour × 8,760 hours = $13,753.20 per year. Space, network, and operations: $1.93/hour × 8,760 hours = $16,906.80 per year. Together those hosting and infrastructure operating costs total $30,660 per year. Total annual Hosted cost = $70,666.67 + $13,753.20 + $16,906.80 = $101,326.67 per year, roughly $11.57 per hour all-in. With frontier bursting, the calculator adds the share you keep on frontier models: annual Hosted cost = $101,326.67 + (frontier % × that year's projected frontier spend). The hourly figures below are rounded for reference; the calculator uses the annual amounts.

   Cost	3-year
   Hardware depreciation	~$8.07/hour
   Power, air-cooled	$1.57/hour
   Space, network, operations	$1.93/hour
   All-in	~$11.57/hour

   Cost per developer-hour

   	At 50 developers	At 30 developers
   Owned, 3-year	$0.23	$0.39

   Developer-hour figures are reference calculations at two team sizes, 30 and 50 developers; they aren't calculator inputs. We assume 8,760 hours in a year and divide the applicable annual cost by total annual developer-hours (8,760 × team size). "Without bursting" is the fixed Hosted cost alone; "+ frontier bursting" adds the frontier share you've kept in your inputs; "Frontier only" is the current-state cost of staying entirely on frontier models. All three use year-one annual costs: "Frontier only" is your monthly spend × 12, so year-over-year growth affects later years in the projections above, not this snapshot.

7. Operating costs ($50,000/yr per node): applied to both newly-purchased and already-owned hardware, because the box has to be operated either way. 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.
8. 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.
9. 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).
10. 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.
11. Sunk-cost hardware path: when GPUs are already available in house, hardware CapEx = $0. Operating costs still apply as an ongoing charge.