IBM & TOGETHER AI $240M INFERENCE ARCHITECTURE

AI Inference Cluster Economics & Capacity Planner

Real-time compute scaling, memory bandwidth bottlenecking & capital payback model
Cluster Architecture Specs H100 SXM5 / B200
GPU Count (Nvidia H100) 8,192 GPUs
Capital Outlay (CaPex) $240M
Amortization Horizon 36 Months
Avg Prompt Length 512 Tokens
Avg Completion Length 256 Tokens
Active Concurrent Streams 250,000
Interconnect Bandwidth 3,200 Gbps
Target Price Metric 1.5M Tokens/$
Unit Inference Cost
$0.18
Per 1,000,000 Generated Tokens
Peak Token Throughput
12.8M t/s
Cluster Aggregate Capacity
Monthly Operating Amortization
$7.85M
CaPex + Cloud Hosting Overhead
Economics Payback
18.4 Mo
Break-even at $0.50/M tokens
Cluster Architecture Topology 8,192 GPUs / 1,024 Nodes
Current Bottleneck State: Memory Bandwidth (KV-Cache Bound)
KV-CACHE LIMIT
Unit Cost Curve vs Concurrency Scale 1.5M Tokens/$ Target
Monthly Token Volume Output: 33.1 Billion Tokens
Deployment Configuration Spec Payload Live Json Schema Output
{ "status": "initializing..." }

AI Inference Cluster Economics & Capacity Planner

Read the explanation

In large-scale AI inference clusters, capital costs and memory bandwidth directly govern serving economics. During token generation, each active stream allocates attention state. Exceeding high bandwidth memory thresholds drops utilization and inflates unit cost. When you slide capital outlay or GPU count, unit cost and payback timeline recalculate immediately across monthly amortizations.

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