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Sizing for AI Applications on Nutanix

Sizing LLM Infrastructures: Key Considerations

As large language models (LLMs) continue to grow in size and complexity, ensuring adequate infrastructure is crucial for efficient deployment and performance. This section explores key considerations when sizing LLM infrastructures, including model storage, GPU requirements, networking throughput, and optimization techniques.

Common Application and Generative AI Use Cases

LLMs are versatile and can be employed in a wide range of AI tasks and applications, including:

  1. Natural Language Processing (NLP): Language translation, text summarization, sentiment analysis, question answering, and text generation.

  2. Conversational AI: Building chatbots, virtual assistants, and dialogue systems for customer service, e-commerce, and personal assistance.

  3. Content Creation: Generating creative content such as articles, stories, scripts, and marketing copy.

  4. Code Generation: Assisting developers by generating code snippets, suggesting improvements, and explaining code functionality.

  5. Multimodal AI: Combining language models with computer vision and speech recognition for tasks like image captioning, video understanding, and audio transcription.

  6. Scientific Research: Analyzing and summarizing large volumes of scientific literature, assisting in hypothesis generation, and supporting drug discovery efforts.

LLM Model Sizes

Recent LLM models like Llama2, Llama3, Mixtral-7B, and Mixtral-8x7B have pushed the boundaries of model size and complexity. These models can range from several gigabytes to hundreds of gigabytes in size, posing significant challenges for storage and computational resources.

For a list of currently supported validated models with Nutanix GPT-in-a-Box (0.2) on Kubernetes, see https://opendocs.nutanix.com/gpt-in-a-box/kubernetes/v0.2/validated_models/

Model Storage

Storing these massive models requires substantial storage infrastructure. For example, the Llama2 model is approximately 7GB, while the Mixtral-8x7B model can reach up to 200GB or more.

Solid-state (SSDs) or Non-Volatile Memory (NVMe) drives are highly recommended for optimal performance and low latency when accessing the underlying model storage.

As with any workload running on Nutanix Infrastructure, sizing the underlying storage infrastructure should consider the following factors:

  • Total model size
  • Number of models to be stored
  • Redundancy and backup requirements
  • Future growth and scalability needs

Additionally, for LLM deployments and AI applications leveraging Retrieval-Augmented Generation (RAG) pipelines should take into consideration additional storage capacity that may be required to handle scenarios that incorporate:

  • Vector Databases: Deploying a vector database like Milvus to store and retrieve relevant information from unstructured data sources should be sized accordingly to the vendor's hardware recommendations when running on Kubernetes.
  • Data Ingestion: Workloads for importing and preprocessing various unstructured data sources into solutions such as Nutanix Objects, such as documents, PDFs, web pages, or misc. multimedia content.

Additionally, the choice of the underlying storage technology being leveraged for model storage plays a crucial role in performance and accessibility.

Here are some options to consider:

  1. Network File System (NFS) (i.e., Nutanix Files): NFS is a traditional file sharing protocol that allows multiple systems to access the same storage over a network. It provides low latency and high throughput but may require additional infrastructure and management overhead.

When deploying Nutanix GPT-in-a-Box, the getting started guide leverages Nutanix Files, but alternative approaches can be considered for advanced use cases.

  1. S3 Object Storage (i.e., Nutanix Objects): Nutanix Objects is a software-defined object storage solution that offers scalable and cost-effective storage for large datasets.

  2. OCI Container Image Registry (i.e., Harbor): Any preferred OCI compliant container registry can be used to store and distribute LLM models as container images. This approach can simplify model deployment and management, especially in containerized environments.

GPU Requirements

LLMs are computationally intensive and benefit greatly from GPU acceleration. The choice of GPU depends on the target deployment environment, whether it's an edge device or a data center.

Edge Deployments

For edge deployments, smaller and more power-efficient GPUs like the NVIDIA T4, A10 or L4 series can be suitable options. These GPUs offer a balance between performance and power consumption, making them ideal for environments with infrastructure constraints.

Data Center Deployments

In data center environments, high-performance GPUs like the NVIDIA L40S and H100 are better suited for handling larger LLM models. These GPUs provide massive parallelism and high memory bandwidth, enabling efficient inference and training.

When sizing GPU infrastructure, consider the following factors:

  • Model size and computational requirements
  • Batch size and throughput requirements
  • Scalability and future growth needs
  • Power and cooling requirements

GPU Models for LLM Infrastructures

When sizing LLM infrastructures, the choice of GPU plays a crucial role in determining performance and efficiency. Here, we'll focus on two GPU models from NVIDIA: the L40S and the L4, which are well-suited for both edge deployments and data center environments.

NVIDIA L4

The NVIDIA L4 (GPU Memory: 16GB) is a low-power, high-performance GPU designed for edge computing and embedded applications.

The L40S is an excellent choice for edge deployments where power efficiency and compact form factors are essential. Its low power consumption and high performance make it suitable for tasks like natural language processing, speech recognition, and computer vision.

NVIDIA L40S

The NVIDIA L40S (GPU Memory: 48GB) is a data center-grade GPU designed for high-performance computing and AI workloads.

With its powerful compute capabilities and ample memory, the L4 is well-suited for deploying larger LLM models in data center environments. It can handle computationally intensive tasks such as language model training, inference, and multi-modal AI applications.

Compute Considerations

In addition to GPU resources, the compute infrastructure for LLM deployments should also be carefully planned. Here are some considerations:

Kubernetes Workloads

For containerized LLM deployments, Kubernetes can provide a scalable and flexible platform for managing and orchestrating workloads. When sizing Kubernetes clusters, consider factors such as the number of nodes, CPU and memory requirements, and the ability to scale up or down based on demand.

Additionally, for LLM deployments involving Retrieval-Augmented Generation (RAG) pipelines, you may need to provision additional workloads to handle tasks such as:

  • Vector Database: Deploying a vector database like Milvus to store and retrieve relevant information from unstructured data sources.
  • Data Ingestion: Workloads for importing and preprocessing various unstructured data sources, such as documents, web pages, or multimedia content.

These additional workloads can help enhance the performance and capabilities of your LLM deployment by providing access to relevant external knowledge sources.

Virtualization Specifications

If deploying LLM models on virtual machines (VMs), ensure that the virtualization platform (i.e., Nutanix AHV) can provide the necessary CPU, memory, and GPU resources. Additionally, consider the overhead introduced by virtualization and the potential impact on performance.

System CPU and Memory Considerations

In addition to GPUs, the CPU and system memory also play a crucial role in LLM infrastructures. Here are some considerations:

CPU

While LLMs primarily rely on GPU acceleration, the CPU is still responsible for tasks like data preprocessing, input/output operations, and orchestrating the overall workflow. High-performance CPUs, such as the latest Intel Xeon or AMD EPYC processors, can ensure efficient data handling and minimize bottlenecks.

System Memory

LLMs often require large amounts of system memory to store intermediate data and facilitate efficient data transfer between the CPU and GPU. Sufficient RAM, ideally DDR4 or DDR5, should be provisioned to avoid performance degradation due to excessive paging or swapping.

Networking Throughput

Efficient data transfer is crucial for LLM deployments, especially in distributed or multi-node setups. High-speed networking infrastructure, such as 100Gbps Ethernet, can ensure low latency and high throughput for model transfers and inference requests.

When sizing networking infrastructure, consider the following factors:

  • Number of nodes and their interconnectivity
  • Data transfer requirements (model updates, inference requests)
  • Bandwidth and latency requirements
  • Scalability and future growth needs

Best Practices for Sizing and Optimizing Modern LLM Models

When sizing and optimizing modern large language models (LLMs), there are several best practices to consider:

  1. Rightsizing Resources: Accurately estimate the resource requirements for your LLM deployment based on factors such as model size, batch size, and throughput requirements. Overprovisioning resources can lead to unnecessary costs, while underprovisioning can result in performance bottlenecks.

  2. Scalability and Elasticity: Design your infrastructure to be scalable and elastic, allowing you to easily scale resources up or down based on demand. This can be achieved through containerization, orchestration tools like Kubernetes, and cloud-native architectures.

  3. Distributed Training and Inference: For very large models that exceed the memory capacity of a single GPU, consider distributed training and inference techniques. This involves partitioning the model across multiple GPUs or nodes, enabling efficient processing of larger models.

  4. Model Quantization: Quantize your LLM models to lower precision (e.g., 16-bit or 8-bit) to reduce memory footprint and increase throughput, while maintaining acceptable accuracy levels. Tools like NVIDIA TensorRT and AMD ROCm can assist with quantization and optimization.

  5. Optimized Data Pipelines: Ensure efficient data ingestion, preprocessing, and transfer pipelines to minimize bottlenecks and latency. Leverage techniques like caching, prefetching, and parallel data processing.

  6. Hybrid Cloud Architectures: Consider a hybrid cloud approach, where you can leverage both on-premises and cloud resources for optimal performance and cost-efficiency. This can involve running inference on-premises, such as an edge deployment, while offloading training to a datacenter or the cloud (i.e., Nutanix NC2).

  7. Monitoring and Optimization: Continuously monitor your LLM infrastructure for performance bottlenecks, resource utilization, and cost optimization opportunities. Leverage monitoring tools and performance profiling to identify areas for improvement.

  8. Automated Scaling and Optimization: Implement automated scaling and optimization mechanisms to dynamically adjust resources based on workload demands. This can involve auto-scaling groups, load balancing, and automated model optimization techniques.

  9. Containerization and Reproducibility: Package your LLM models and dependencies into containers for consistent and reproducible deployments across different environments. This can simplify management, updates, and portability.

  10. Collaboration and Knowledge Sharing: Foster collaboration and knowledge sharing within your organization and the broader LLM community. Stay up-to-date with the latest best practices, tools, and techniques for optimizing LLM deployments.

By carefully considering these factors and employing appropriate optimization techniques, organizations can effectively size and optimize their LLM infrastructures, ensuring efficient performance, scalability, and cost-effectiveness while meeting the demanding requirements of modern LLM applications.