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Telecom / Network Planning · Free Tool
AI Data Center Interconnect (DCI) Bandwidth Planner
See what scale-up and scale-out network bandwidth an AI deployment actually demands — before the RFP gets written. Based on publicly published accelerator interconnect generations and distributed-training traffic patterns.
4 sections · ~12 questions
~3 minutes
Personalized action plan
PDF summary included
Vendor-neutral · Free
Important disclaimer: This calculator produces illustrative, standards-based bandwidth and latency estimates using publicly published accelerator interconnect specifications and distributed-training traffic patterns. Results are indicative only and intended for early-stage capacity planning. Validate exact requirements with network and ML systems engineers before writing procurement specifications.
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Phase 1 · AI Cluster Profile
Sets the scale for the deployment you're planning network capacity for — your own AI infrastructure, or a customer's.
Total AI accelerators (GPUs or equivalent) in the largest single cluster.
Physical locations this deployment spans. Use 1 for a single-site cluster.
Phase 2 · Workload Mix
Training and inference place very different demands on scale-up and scale-out bandwidth.
Training synchronization traffic is the primary driver of cross-site DCI bandwidth need.
0% (all inference)100% (all training)
50% training / 50% inference
Larger models drive proportionally more gradient-synchronization and replication traffic.
Different parallelism strategies cross site boundaries very differently.
Phase 3 · Topology Today
Your current scale-up and inter-site connectivity — this is the baseline the gap analysis is measured against.
In-cluster accelerator interconnect fabric today.
How sites are connected today, if at all.
Approximate capacity per inter-site link today. Leave blank if none / not sure — we'll assume 0.
Leave blank if unknown — we'll assume a typical regional-distance figure (10ms).
Phase 4 · Assumption Calibration
Adjust growth and redundancy assumptions used to check whether today's topology covers near-term growth.
Methodology Reference
Bandwidth figures are drawn from publicly published AI accelerator interconnect generations and well-documented distributed-training traffic patterns (data/model/pipeline parallelism). Defaults below are typical planning assumptions — adjust to match your environment.
Applied to today's accelerator count and cross-site traffic volume.
10%150%
50%
Extra margin on top of projected growth, for failover and burst capacity.
0%50%
15%
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AI DCI Bandwidth Planner
Your Inputs & Assumptions
Methodology & Sources
1Accelerator interconnect tiers — illustrative bandwidth bands based on publicly published AI accelerator interconnect generations across major hardware vendors, generalized rather than tied to a specific product.
2Distributed training traffic patterns — data-parallel, model-parallel, and pipeline-parallel synchronization traffic profiles are well-documented ML systems patterns; cross-site traffic estimates are directional planning figures, not precise engineering specifications.
3Latency feasibility thresholds — rule-of-thumb round-trip latency bands for synchronous multi-site training vs. asynchronous/inference traffic, framed as a feasibility flag rather than a guaranteed engineering outcome.
4TeckNexus Intelligence & Industry Reports, 2026 — tool design, defaults, and framing.
Important Disclaimer
This report produces illustrative bandwidth and latency estimates based on simplified, publicly-published-specification assumptions and your self-reported inputs. It is provided for indicative early-stage planning purposes only.
This calculator does not constitute network engineering, procurement, or investment advice. Actual requirements depend on your specific accelerator hardware and interconnect generation, cluster topology, workload characteristics, and vendor-specific implementation. Bandwidth figures shown are simplified planning references, not exact engineering specifications.
TeckNexus has not verified site-specific conditions. Users are advised to commission vendor-specific capacity planning and engineering validation, including from network and ML systems engineers, before making network design or procurement decisions.
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Talk to a network specialist about your AI infrastructure connectivity and validate this estimate against your specific environment.
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