Running Build Your Trace as a service, Course Creation

Build Your Trace Agency Implementation, Training Infrastructure Consulting

Learn to deliver distributed AI training optimization services by mastering trace reconstruction, parallelism strategy analysis, and collective primitive tuning. Agencies guide ML teams through interactive exercises that compress learning cycles on tensor parallelism, FSDP, and expert parallelism without expensive GPU experimentation.

Open the decision record for Build Your Trace

What does running Build Your Trace for clients commit you to?

Published figures for this service. Blank fields are not published.

Monthly tool cost
Vendor software cost: $0 (open-source pricing model). Agency labor, onboarding, and infrastructure costs are Not modeled because no vendor setup fee, client price, or labor rate is published in the supplied data.
Time to first value
Hours (per published time_to_value); setup_complexity is low
Payback
Not modeled
Guided implementation
8 hours

Is Build Your Trace worth running as a client service?

Build Your Trace is a free, open-source educational platform for distributed AI training optimization, with enough structure (15 trace builds, 8 comparison labs, 7 recognition rounds) to support a facilitated upskilling service. Because the agency cannot rebrand it and no client price, labor, usage, overhead, or volume data is supplied, ROI and margin remain unproven until the agency supplies those inputs.

An agency-fit judgement for reselling this service. It is separate from the tool description on the decision record.

Before you start

What has to be in place before the first client engagement.

Tools and subscriptions

  • Build Your Trace open-source platform access
  • GitHub access for the Build Your Trace repository
  • Reference hardware specification data such as H100 SXM compute, memory, and interconnect bandwidths
  • Reference model configuration data such as Qwen3-235B-A22B layer count, hidden size, heads, and experts
  • Client training trace data or documented aggregate envelopes for measured tasks

People and inputs

  • Staff familiar with distributed training parallelism strategies TP, FSDP, EP, and CP
  • Trainer or facilitator to guide the 15 trace builds, 8 comparison labs, and 7 recognition rounds
  • Internal environment for reviewing the interactive collective primer and timing model
  • Technical account manager for AI training teams and performance optimization specialists

Included with the course

6 working documents for delivering this service.

  • Client Onboarding Checklist for Trace Analysis Projectschecklist
  • Parallelism Strategy Comparison Lab Templatetemplate
  • GPU Trace Reconstruction SOPsop
  • Collective Primitive Tuning Worksheetworksheet
  • Hardware Specification Reference Guide for H100 and A100guide
  • Dependency Identification Diagnostic Worksheetworksheet

Listed by name. These documents are not yet published as individual downloads.