Project

Ascend 910C Training and Inference Diagnostics

Team-based LLM infrastructure work covering training diagnosis, benchmark selection, and an operator compatibility fix.

Python · Ascend 910C · LLM Training · Operator Compatibility · HCCL

2026

Product Snapshot

Role: Engineering contributor / team project
Stage: Training and single-node inference reached staged validation; a distributed HCCL deadlock remained open.
Focus: Training performance diagnosis, benchmark selection, input-format compatibility, and operator routing.
Validation: Personally implemented dynamic routing for three input formats; team records report MFU improving from about 2% to 25%-30%.
Public Proof: Internal diagnosis notes, fix records, and team run records; performance figures were not independently rerun for this portfolio.

Next Step

Portfolio PDF About the Founder Email

Public scope

This was an LLM infrastructure project led by a partner team. To protect confidential context, this page covers only the technical issue, my attributable contribution, and known limits. It omits the partner name, hardware scale, and internal plans.

My contribution

I worked on training-performance diagnosis, benchmark selection and code analysis, and an operator bug caused by hard-coded input unpacking.

The fix replaced a single-format path with dynamic routing across three input formats, allowing the single-node inference flow to proceed through later operator checks.

Team result and attribution

Team run records report that training MFU improved from roughly 2% to 25%-30%. That is a team result, not an individual claim. My attributable scope is diagnosis support, benchmark analysis, and the compatibility fix.

What remained open

A passing single-node path is not a finished distributed system. An HCCL distributed deadlock remained unresolved in the available record, so this page does not claim complete production stability.

Engineering lesson

Many apparent model failures are contract failures between frameworks, operators, and multiple data formats. Explicit input boundaries can be as important as the model itself.