Build Your Openclaw Agent with Multi-Modal Models
Abstract
This is a beginner level hands-on class which will show you how to build your own Openclaw agent using opensource multi-modal models. This course will also show you how to set up an opensource model server with vLLM/SGLang and connect your agent to it.
July 22, 2026 09:30 - 10:15
Speakers
Presented By
SMTS Product Application Engineer | AMD
SMTS Software Development Engineer | AMD
Session Type
Workshop
Related Product
Instinct, EPYC, ROCm
Related Sessions
-
Training at Scale with AMD Primus
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. Primus’ SOTA pre and post training performance, proven at scales of thousands of GPUs, positions instinct 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. Primus’ SOTA pre and post training performance, proven at scales of thousands of GPUs, positions instinct as a competitive solution for model development at frontier labs, enterprises and AI startups.
July 23, 2026
-
Benchmarking AI Systems: from Model Metrics to Real-World Performance
Benchmarking AI Systems: from Model Metrics to Real-World Performance
The agentic AI stack has evolved to fast multi-model orchestration, tool-augmented reasoning, and long-running inference chains. The hardware conversation hasn't kept up, and many teams default to one GPU vendor without evaluating alternatives. This interactive session is for builders to learn what they're missing. We'll review head-to-head benchmark data from third-party testing, discuss production-ready serving stacks on ROCm, and break down TCO for teams running multi-step agents at scale.;The agentic AI stack has evolved to fast multi-model orchestration, tool-augmented reasoning, and long-running inference chains. The hardware conversation hasn't kept up, and many teams default to one GPU vendor without evaluating alternatives. This interactive session is for builders to learn what they're missing. We'll review head-to-head benchmark data from third-party testing, discuss production-ready serving stacks on ROCm, and break down TCO for teams running multi-step agents at scale.
July 23, 2026
-
Agentic Kernel Performance Tuning with AMD ROCm
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
-
Efficient LLM Serving at Scale with Unified Caching
Efficient LLM Serving at Scale with Unified Caching
This is an advanced user hands-on workshop to show TensorMesh and AMD enabling efficient LLM serving through an unified caching layer. You will learn how tiered KV cache management can brings out the benefits of cache-aware inference, improving throughput under interactive latency SLAs, reducing TTFT through KV cache reuse/offload & enabling production-style distributed inference on Instinct GPUs.;This is an advanced user hands-on workshop to show TensorMesh and AMD enabling efficient LLM serving through an unified caching layer. You will learn how tiered KV cache management can brings out the benefits of cache-aware inference, improving throughput under interactive latency SLAs, reducing TTFT through KV cache reuse/offload & enabling production-style distributed inference on Instinct GPUs.
July 23, 2026
-
Power is Your Biggest Hidden Cost: How AMD Can Help
Power is Your Biggest Hidden Cost: How AMD Can Help
Power is the AI infrastructure cost nobody budgets for until it breaks the business case. In this interactive technical session, an expert from 5C joins AMD to unpack how power consumption impacts total cost of ownership across inference and training deployments. Discuss how intelligent power management, real-world thermal constraints, and silicon-level efficiency shape what your AI infrastructure can sustain. Practical insight for architects and operators making deployment decisions today.;Power is the AI infrastructure cost nobody budgets for until it breaks the business case. In this interactive technical session, an expert from 5C joins AMD to unpack how power consumption impacts total cost of ownership across inference and training deployments. Discuss how intelligent power management, real-world thermal constraints, and silicon-level efficiency shape what your AI infrastructure can sustain. Practical insight for architects and operators making deployment decisions today.
July 23, 2026
-
Accelerating LLM Inference on AMD ROCm with AITER and ATOM
Accelerating LLM Inference on AMD ROCm with AITER and ATOM
This technical talk introduces AITER and ATOM, optimized inference technologies for AMD ROCm software. Learn how AITER accelerates LLM and MoE execution with optimized kernels and distributed inference enhancements, while ATOM integrates these capabilities into familiar vLLM and SGLang workflows through plugin-based acceleration. The session highlights how AMD enables scalable, high-performance open-source LLM serving while preserving existing developer and deployment workflows.;This technical talk introduces AITER and ATOM, optimized inference technologies for AMD ROCm software. Learn how AITER accelerates LLM and MoE execution with optimized kernels and distributed inference enhancements, while ATOM integrates these capabilities into familiar vLLM and SGLang workflows through plugin-based acceleration. The session highlights how AMD enables scalable, high-performance open-source LLM serving while preserving existing developer and deployment workflows.
July 23, 2026
-
Advantages of Deploying Agentic AI at Work with Agent Computers
Advantages of Deploying Agentic AI at Work with Agent Computers
Agentic AI is redefining the future of work by moving computing beyond user-driven interactions to intelligent agents that can reason, plan, and execute ta;Agentic AI is redefining the future of work by moving computing beyond user-driven interactions to intelligent agents that can reason, plan, and execute tasks autonomously. Discover how agent computers powered by AMD Ryzen™ AI PRO processors are unlocking a new era of enterprise productivity through faster, more responsive, and more secure local AI workflows.
July 23, 2026
-
Transformation of AMD ROCm Software in a New AI Era
Transformation of AMD ROCm Software in a New AI Era
This session explores an AI-native GPU software stack for large-scale AI systems on AMD hardware. Learn how AI-assisted GPU programming, distributed training, optimized inference, memory expansion, and agentic deployment workflows are enabling scalable AI infrastructure across clusters and hyperscale environments. The talk highlights practical approaches for improving performance, observability, automation, and resource efficiency on the AMD GPU platforms.;This session explores an AI-native GPU software stack for large-scale AI systems on AMD hardware. Learn how AI-assisted GPU programming, distributed training, optimized inference, memory expansion, and agentic deployment workflows are enabling scalable AI infrastructure across clusters and hyperscale environments. The talk highlights practical approaches for improving performance, observability, automation, and resource efficiency on the AMD GPU platforms.
July 23, 2026