---
title: "Domain-Specific AI Training at Scale: Lessons from Telecom"
description: This webinar examines the shift from general-purpose large language models to domain-specific AI training, using telecom as a case study. Panelists from AT&T, TensorWave, and AMD discuss how organizations with specialized datasets can achieve better cost, latency, and accuracy by training smaller, purpose-built models rather than relying solely on frontier models.
image: https://webinars.techstronglearning.com/hubfs/AMD-3-Domain-Specific%20AI%20Training%20at%20Scale_Lessons%20from%20Telcom1080x1080.png
---

Domain-Specific AI Training at Scale: Lessons from Telecom

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This webinar examines the shift from general-purpose large language models to domain-specific AI training, using telecom as a case study. Panelists from AT&T, TensorWave, and AMD discuss how organizations with specialized datasets can achieve better cost, latency, and accuracy by training smaller, purpose-built models rather than relying solely on frontier models.

AT&T describes practical deployments ranging from sub-gigabyte on-device models for offline customer support to larger models fine-tuned for network root cause analysis. A key theme is flexibility: using ScalarLM, an open-source GPU-agnostic training framework, AT&T has seamlessly moved model checkpoints between AMD Instinct and NVIDIA hardware. TensorWave details the infrastructure behind this work, demonstrating up to 240x training speedup by scaling across 256 GPUs. The speakers also highlight the Open Telco benchmark — a GSMA-backed initiative producing open-source telecom training data and models downloaded over 12 million times.

Panelists agree that open models, shared benchmarks, and hardware-agnostic software stacks are lowering barriers to domain-specific AI across industries, including finance, healthcare, and media — making specialized training an increasingly practical option for any data-rich enterprise. 

![Farbod\_Tavakkoli-modified](https://webinars.techstronglearning.com/hs-fs/hubfs/Farbod_Tavakkoli-modified.png?width=865&height=865&name=Farbod_Tavakkoli-modified.png "Farbod_Tavakkoli-modified")

### Farbod Tavakkoli

###### Data Scientist, AT&T

Farbod is a Data Scientist at AT&T and the technical lead of the Open Telco AI project (OTel), an open-source initiative delivering curated telecom datasets and a family of telecom-specialized language, embedding, and reranking models. The released OTel models have surpassed 16 million downloads and received 157+ pieces of media coverage worldwide. His work focuses on training and deploying domain-specific large language models at scale across both AMD and NVIDIA hardware, spanning the full generative AI lifecycle from data curation and model design to evaluation, benchmarking, deployment, and open-source release.

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### Gregory Diamos

###### ScalarLM Architect, TensorWave

Greg is a distinguished engineer at Relational AI and ScalarLM architect. Previously Greg was a founder of MLPerf™, the industry standard benchmark for deep learning performance, and several AI startups. He was a founding engineer at Baidu’s Silicon Valley AI Lab (SVAIL), where he co-invented and wrote the framework for the first 1,000 CUDA GPU training cluster and trained the first deep learning speech and language model deployed in production to billions of users.

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### Kumaran Siva

###### Corporate Vice President, Strategic Business Development, AMD

Kumaran Siva is a Corporate Vice President responsible for the Software & Systems Business Development inside the Server Business Unit at AMD based in Santa Clara, California. In this role, he is responsible for strategic business development and product management of AMD’s software and systems solutions for AMD EPYC™ processors. Prior to this role, Kumaran managed the Cloud Business group responsible for the hyperscale business.

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### Guy Currier

###### Research Director - The Futurum Group

 Guy is responsible for positioning, go-to-market, and sales guidance across technologies and markets. He has decades of field experience describing technologies, their business and community value, and how they are evaluated and acquired. Guy’s specialty areas include AI, DevOps/cloud-native, enterprise applications, application integration, Big Data, governance-risk-compliance, containerization, virtualization, HPC, CPUs-GPUs-xPUs, and systems lifecycle management.

Guy started his technology career as a research director for technology media company Ziff Davis, with stints at PC Magazine, eWeek, and CIO Insight. Prior to joining Visible Impact, he worked at Dell, including postings in marketing, product, and technical marketing groups for a wide range of products, including engineered systems, cloud infrastructure, enterprise software, and mission-critical cloud services. He lives and works in Austin, TX.  

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