Posts Tagged ‘AIInfrastructure’
[AWSReInvent2025] The Physics of Scaling: Advanced Methodologies for Large-Scale AI Training on AWS
Lecturer
Tomori is a Senior Specialist Solutions Architect at Amazon Web Services (AWS), specializing in high-performance computing (HPC) and distributed machine learning. Tomori’s research and professional focus center on the “physics” of training—optimizing the interaction between silicon, networking fabrics, and software frameworks to enable the training of models with trillions of parameters.
Abstract
Training state-of-the-art AI models has become a monumental engineering challenge that requires the precise orchestration of massive compute clusters. This article explores the technical foundations of training high-performing AI models at scale using AWS’s purpose-built infrastructure. It provides an in-depth analysis of Amazon SageMaker HyperPod and its role in distributed training, covering essential techniques such as data and model parallelism. The discussion highlights the shift from managed APIs to Kubernetes-based orchestration (EKS) and Slurm for high-performance job scheduling. Key innovations in automated model tuning, observability through Prometheus and Grafana, and efficient checkpoint management are detailed, showing how these strategies can reduce training time by up to 40% while optimizing costs for the next generation of foundation models.
Scaling Challenges in the Era of Foundation Models
The shift toward foundation models has fundamentally changed the requirements for AI training infrastructure. When a model’s memory footprint exceeds the capacity of a single GPU, developers must move beyond simple training scripts to sophisticated distributed systems. The challenges are three-fold: compute intensity, memory constraints, and networking bottlenecks.
At scale, the “efficiency of the cluster” becomes the primary metric. Organizations must navigate the trade-offs between different types of parallelism—Data Parallelism, where the dataset is split across nodes, and Model Parallelism (including Pipeline and Tensor parallelism), where the model itself is distributed. AWS addresses these challenges by providing a specialized infrastructure layer, specifically through P5 instances powered by NVIDIA H100 GPUs and high-bandwidth Elastic Fabric Adapter (EFA) networking. This hardware foundation is critical for maintaining the high-speed communication necessary for synchronization during the backward pass of training.
Orchestration Choice: Kubernetes vs. Slurm
A critical decision for any organization training at scale is the choice of orchestrator. AWS offers two primary paths for managing SageMaker HyperPod clusters, each catering to different operational philosophies.
- Kubernetes (Amazon EKS): This is often the preferred choice for organizations that have adopted a cloud-native DevOps culture. Kubernetes provides a robust API for managing containers, and when integrated with SageMaker HyperPod, it allows for the orchestration of ML training jobs alongside other microservices. This path benefits from a rich ecosystem of observability tools like Prometheus and Grafana.
- Slurm: Traditionally used in high-performance computing (HPC) environments, Slurm is a highly efficient job scheduler designed for massive, tightly coupled workloads. Organizations coming from research backgrounds or those requiring the absolute lowest overhead often choose Slurm.
Regardless of the orchestrator, SageMaker HyperPod provides the underlying “resiliency layer,” which includes automated node health monitoring and the ability to replace failing instances without human intervention. This ensures that the long-running training “epochs” are not disrupted by hardware variability.
Parallelism Strategies and Performance Optimization
Achieving a 40% reduction in training time requires more than just more GPUs; it requires optimized parallelism strategies. The discussion centers on three primary methodologies:
- Data Parallelism: The most common approach, where each GPU processes a different batch of data and gradients are synchronized.
- Tensor Parallelism: Splitting individual layers across multiple GPUs to handle extremely wide layers.
- Pipeline Parallelism: Dividing the model into stages and processing them in a “pipeline” fashion to maximize GPU utilization.
SageMaker’s distributed training libraries automate much of this complexity. By injecting “sharding” logic into the training script, these libraries allow developers to scale their models with minimal code changes. Furthermore, the use of automated model tuning—which systematically explores the hyperparameter space—ensures that the resulting model reaches its peak performance without manual “trial and error.”
Code Sample: Configuring Model Parallelism in SageMaker
from sagemaker.pytorch import PyTorch
'''
Configuration for SageMaker Model Parallelism (SMP)
'''
smp_options = {
"enabled": True,
"parameters": {
"pipeline_parallel_degree": 2,
"tensor_parallel_degree": 4,
"optimize": "speed",
"ddp": True
}
}
'''
Training job configuration using HyperPod
'''
estimator = PyTorch(
entry_point='train_llm.py',
instance_type='ml.p5.48xlarge',
instance_count=16, # Scaling to 128 GPUs
distribution={"smdistributed": {"modelparallel": smp_options}},
role=sagemaker.get_execution_role(),
framework_version='2.1'
)
estimator.fit()
Observability and Fault Tolerance
In a cluster of hundreds of GPUs, “observability” is not a luxury—it is a requirement. Tomori emphasizes the importance of a centralized observability stack using Prometheus and Grafana. These tools allow engineers to monitor GPU utilization, memory pressure, and network throughput in real time. If a node begins to underperform or “throttles” due to heat, the observability stack provides the alerts necessary for proactive management.
Coupled with observability is the concept of efficient checkpoint management. By utilizing high-performance storage like Amazon FSx for Lustre, SageMaker HyperPod can save model “weights” and optimizer states rapidly. In the event of a failure, the system can reload the most recent checkpoint and resume training within minutes. This “resilient training” loop is what enables the massive foundation models of today to be trained in weeks rather than months.
Conclusion
Training high-performing AI models at scale is as much an infrastructure challenge as it is an algorithmic one. Through the integration of SageMaker HyperPod, optimized parallelism libraries, and robust orchestration via EKS or Slurm, AWS has created an environment where “the physics of scaling” can be mastered. By reducing training times and automating resiliency, these tools empower organizations to push the boundaries of what is possible, turning the promise of trillion-parameter models into a repeatable, cost-effective reality.
Links:
[VoxxedDaysAmsterdam2026] Framework Desktop and Incus OS: An Efficient Setup for Local LLM Deployment
Lecturer
Peter Smink works with Team Roxy and collaborates with AMD on advanced hardware solutions. He focuses on practical approaches to running AI workloads locally, emphasizing privacy, cost control, and energy efficiency through modern container and virtualization technologies.
Abstract
Running large language models locally addresses critical concerns around data privacy, operational costs, and environmental impact, yet requires careful hardware and software configuration. Peter Smink presents the combination of Framework Desktop hardware with Incus OS as a compelling solution for local LLM deployment. The discussion covers the advantages of this setup, practical implementation steps, encountered challenges, and real-world performance characteristics. Through detailed examination of the installation process, GPU passthrough configuration, and model execution, the presentation demonstrates how this approach enables efficient, private, and sustainable AI development while maintaining flexibility for broader container and virtual machine workloads.
Advantages of Local LLM Deployment with Modern Hardware and Virtualization
Local execution of large language models offers distinct benefits compared to cloud-based alternatives. Privacy concerns are eliminated when sensitive data never leaves organizational infrastructure. Operational costs become predictable without recurring API charges or token-based billing. Energy consumption can be monitored and optimized at the hardware level, supporting sustainability goals. Additionally, local setups provide greater control over model selection and experimentation, unconstrained by provider limitations or network dependencies.
The Framework Desktop platform, powered by AMD Ryzen processors with integrated AI capabilities, delivers high performance within a compact and power-efficient form factor. Configurations supporting up to 128 GB of memory and efficient power envelopes ranging from 80 watts upward make it suitable for demanding workloads while maintaining reasonable energy profiles. The modular design allows for future upgrades and ensures hardware longevity beyond specific AI use cases.
Incus OS complements this hardware by providing a modern, secure, and flexible virtualization and containerization layer. Built on immutable Linux foundations with secure boot and TPM support, it offers robust isolation for workloads. The system includes built-in update mechanisms and supports both containers and virtual machines through a unified command-line interface. This versatility allows teams to run AI inference alongside other development or production services on the same infrastructure.
Implementation Process and Configuration Details
Setting up the environment begins with client preparation. The Incus client must be installed and configured with appropriate certificates for secure communication with the target system. This client serves as the primary interface for managing the remote Incus instance.
Image creation follows, utilizing the flasher tool to generate a customized Incus OS image. Configuration files specify critical parameters including the target disk, graphics drivers for AMD hardware, and PCI passthrough settings for GPU access. The process ensures that the resulting image includes necessary drivers and security configurations tailored to the Framework Desktop hardware.
On the hardware side, BIOS preparation involves enabling secure boot and clearing existing certificates to accommodate the new installation. CPU and memory settings are optimized for the installation phase. Once the USB image is created and booted, the automated installation process configures the system according to the provided specifications.
Post-installation steps focus on network configuration and virtual machine setup. A dedicated network is established for VM connectivity. The AI server virtual machine is then created with substantial memory allocation and direct GPU passthrough. This configuration enables the virtual machine to leverage hardware acceleration for model inference.
Within the virtual machine, environment preparation includes kernel updates, installation of necessary tools, and configuration of user groups for GPU access. The latest AMD graphics drivers ensure optimal performance. Verification steps confirm that the GPU is properly recognized and accessible to the inference software.
Operational Characteristics and Performance Considerations
The resulting setup demonstrates strong performance for local inference tasks. Token generation rates vary based on model size and configuration, with observed averages ranging from 25 to 60 tokens per second and peaks approaching 90 tokens per second under optimal conditions. Power consumption remains efficient, typically between 18 and 130 watts depending on workload intensity.
The combination supports models that may exceed the capacity of individual graphics cards by leveraging system memory and CPU resources effectively. Privacy is maintained as all processing occurs locally without external data transmission. Cost predictability eliminates concerns about variable cloud pricing or usage spikes.
The platform’s reusability adds significant value. Should AI-specific requirements evolve, the hardware remains fully functional as a general-purpose server or development workstation. This contrasts with specialized AI accelerators that may become obsolete or underutilized outside narrow use cases.
Challenges and Lessons Learned
Several practical challenges emerge during implementation. Certificate and client configuration require precise sequencing to ensure secure connectivity. Incorrect order or missing privileges can result in systems that fail to communicate properly. GPU passthrough configuration demands careful attention to hardware and driver compatibility.
Kernel updates and driver installations must align with the specific hardware platform. Recent changes in driver APIs have occasionally created compatibility hurdles, though newer versions have improved stability significantly. Memory and storage requirements for both the host system and virtual machines necessitate careful planning to avoid resource contention.
Despite these considerations, the overall setup process proves manageable with appropriate documentation and systematic verification at each stage. The modular nature of both hardware and software components allows for iterative refinement as requirements evolve.
Strategic Value for AI Development and Beyond
This hardware and software combination addresses multiple strategic objectives simultaneously. Privacy-conscious organizations gain a viable alternative to cloud services without sacrificing performance. Development teams benefit from rapid iteration cycles and direct hardware access for experimentation. Cost-sensitive projects maintain predictable operational expenses while avoiding vendor lock-in.
The solution extends beyond pure AI inference. The underlying Incus platform supports diverse workloads, making the infrastructure adaptable to changing organizational needs. Teams can experiment with different models, quantization techniques, and inference engines while maintaining consistent operational procedures.
Energy efficiency aligns with growing sustainability requirements in technology operations. The ability to monitor and control power consumption at the hardware level supports both environmental goals and operational cost management.
As AI adoption continues across industries, solutions that balance capability, control, and efficiency become increasingly valuable. The Framework Desktop paired with Incus OS represents one practical approach to achieving these objectives while maintaining flexibility for future requirements.