Skip to main content

Real2Sim for Embodied Data Generation

abstract background

This beginner-to-intermediate workshop introduces a practical pipeline for building scalable embodied Synthetic Data generation systems for robotics and physical AI. Participants will learn how to transform real-world objects into interaction-ready simulation assets through a Real2Sim workflow focused on object-centric reconstruction, and simulation integration on AMD Instinct GPUs.

July 22, 2026 2:30 PM - 3:15 PM PDT

David Li
David Li

SMTS Product Application Engineer | AMD

Alex He
Alex He

SMTS Product Application Engineer | AMD

Topic

AI Training & Inference

Physical AI & Robotics

Session Type

Workshop

Related Product

Instinct, Radeon, ROCm, EPYC

Related Sessions

View All Sessions
  • Domain-Specific AI at Scale: Open Models, Post-Training, and AI Infrastructure

    Learn how domain-specific AI moves beyond generic models using post-training, domain evals, and scalable open infrastructure. Using Open Telco Models as a case study, this session covers curated data, reward loops, unified training and serving, and AMD Instinct/ROCm-based stacks for building specialized AI systems at enterprise scale.;Learn how domain-specific AI moves beyond generic models using post-training, domain evals, and scalable open infrastructure. Using Open Telco Models as a case study, this session covers curated data, reward loops, unified training and serving, and AMD Instinct/ROCm-based stacks for building specialized AI systems at enterprise scale.

    July 23, 2026

  • Training at Scale with AMD Primus

    Primus makes large-scale training on Instinct reliable, debuggable and highly performant. It supports the latest OSS training frameworks, models, and is expanding support to new, cutting-edge model architectures, training techniques, and datatypes. SOTA pre and post training performance with Primus, proven at scales of thousands of GPUs, positions an AMD Instinct GPU as a competitive solution for model development at frontier labs, enterprises, and AI startups.;Primus makes large-scale training on Instinct reliable, debuggable and highly performant. It supports the latest OSS training frameworks, models, and is expanding support to new, cutting-edge model architectures, training techniques, and datatypes. SOTA pre and post training performance with Primus, proven at scales of thousands of GPUs, positions an AMD Instinct GPU as a competitive solution for model development at frontier labs, enterprises, and AI startups.

    July 23, 2026

  • Scaling AI in Production with Vultr

    Explore how enterprises can scale AI from training to inference using AMD-powered infrastructure on Vultr. Through a deep dive into the University of Cambridge's Tessera model, learn how organizations can accelerate AI deployment, improve operational efficiency, and scale globally. The session also highlights real-world AI initiatives across healthcare, retail, finance, manufacturing, and hospitality.;Explore how enterprises can scale AI from training to inference using AMD-powered infrastructure on Vultr. Through a deep dive into the University of Cambridge's Tessera model, learn how organizations can accelerate AI deployment, improve operational efficiency, and scale globally. The session also highlights real-world AI initiatives across healthcare, retail, finance, manufacturing, and hospitality.

    July 23, 2026

  • Lessons Learned Training ZAYA1-74B End-to-End on AMD

    ZAYA1-74B is a 74B-parameter MoE (4B active) pretrained and post-trained end-to-end on AMD MI300X. We share what it took: a co-designed stack with CCA attention, expert-context-parallel folding, and sliding-window layers that halve KV cache, plus an RL pipeline that sharpens math and code and hardens multi-turn agentic tool use. The result is a competitive long-context, Apache 2.0 model, proving the full train-to-RL loop runs effectively on AMD.;ZAYA1-74B is a 74B-parameter MoE (4B active) pretrained and post-trained end-to-end on AMD MI300X. We share what it took: a co-designed stack with CCA attention, expert-context-parallel folding, and sliding-window layers that halve KV cache, plus an RL pipeline that sharpens math and code and hardens multi-turn agentic tool use. The result is a competitive long-context, Apache 2.0 model, proving the full train-to-RL loop runs effectively on AMD.

    July 23, 2026

  • Agentic Kernel Performance Tuning with AMD ROCm

    This session introduces an agentic kernel development workflow for optimizing AI and HPC workloads on AMD ROCm. Learn how a self-directing optimization loop can profile, analyze, optimize, validate, and generate production-ready kernel improvements with minimal manual tuning. The talk highlights how AMD is accelerating kernel engineering by reducing weeks of performance optimization effort into an automated, scalable workflow for developers and performance engineers.;This session introduces an agentic kernel development workflow for optimizing AI and HPC workloads on AMD ROCm. Learn how a self-directing optimization loop can profile, analyze, optimize, validate, and generate production-ready kernel improvements with minimal manual tuning. The talk highlights how AMD is accelerating kernel engineering by reducing weeks of performance optimization effort into an automated, scalable workflow for developers and performance engineers.

    July 23, 2026

  • Dell Technologies: Balancing CPU and GPU for the Agentic AI Era

    Agentic AI changes the infrastructure equation. These workloads plan, branch, call tools, retrieve data, manage long context, and re-plan in real time, creating far greater CPU demand than traditional GPU-heavy inference. In this Meet the Expert session, learn why balanced Dell PowerEdge platforms with 1:1 vs. 1:8 AMD EPYC CPUs and AMD Instinct MI350P GPUs are critical to improving latency, GPU utilization, and efficiency for production-ready agentic AI.;Agentic AI changes the infrastructure equation. These workloads plan, branch, call tools, retrieve data, manage long context, and re-plan in real time, creating far greater CPU demand than traditional GPU-heavy inference. In this Meet the Expert session, learn why balanced Dell PowerEdge platforms with 1:1 vs. 1:8 AMD EPYC CPUs and AMD Instinct MI350P GPUs are critical to improving latency, GPU utilization, and efficiency for production-ready agentic AI.

    July 23, 2026

  • ROCm AMD Infinity Context: Shared KV Cache for Agentic AI

    As LLM inference shifts toward long context, multi-turn sessions, and agents, KV cache becomes a dominant scaling constraint that no longer fits in GPU HBM. Existing caching solutions partially solve this using expensive CPU memory, non-optimized remote storage or local non-sharable NVMe SSDs. ROCm AIC proposes a shared-across-GPUs, low-latency storage tier to offload KV cache. This session gives insight into the ROCm AIC architecture and provides measured performance, backed by partner vendors.;As LLM inference shifts toward long context, multi-turn sessions, and agents, KV cache becomes a dominant scaling constraint that no longer fits in GPU HBM. Existing caching solutions partially solve this using expensive CPU memory, non-optimized remote storage or local non-sharable NVMe SSDs. ROCm AIC proposes a shared-across-GPUs, low-latency storage tier to offload KV cache. This session gives insight into the ROCm AIC architecture and provides measured performance, backed by partner vendors.

    July 23, 2026

  • Accelerating Inference at Scale: Crusoe's Experience with AMD

    As a customer and operator of AMD technology, Crusoe’s Managed Inference team has built a production inference stack designed for speed, efficiency, and scale. This session will show how AMD Instinct, including MI355X, helped shape its serverless inference offering and what teams can apply when building production AI services that balance performance, memory bandwidth, and cost.;As a customer and operator of AMD technology, Crusoe’s Managed Inference team has built a production inference stack designed for speed, efficiency, and scale. This session will show how AMD Instinct, including MI355X, helped shape its serverless inference offering and what teams can apply when building production AI services that balance performance, memory bandwidth, and cost.

    July 23, 2026