October 1, 2024
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When Meta does development at scale, you take notes because they're setting the standard for AI at scale. The way Meta tackles the challenges of training LLMs at scale is what growing businesses can learn from. As the need for more resources grew, Meta shifted from training smaller models to handling massive LLMs with innovative hardware and software solutions. Meta’s ability to overcome scaling challenges while maintaining efficiency and accuracy sets it apart. This case study explores how Meta’s use of advanced hardware, network optimisation and storage solutions provides valuable lessons for companies aiming to scale their AI infrastructure.
Challenges Faced by Meta to Scale AI Training
Scaling AI training for GenAI models is a daunting task requiring meticulous planning and precise execution. Let’s look at the challenges Meta faced to scale AI training:
Increasing GPU Numbers Leading to Higher Risk of Hardware Failure
As Meta increased its efforts to deploy LLMs on a large scale, the total number of GPUs required to meet computational demand escalated. Unfortunately, increasing the number of GPUs also raised the probability of hardware failure, which could seriously disrupt AI training. When hundreds or even thousands of GPUs are used at the same time, failures can become catastrophic, requiring sophisticated backup plans and redundancy mechanisms.
Efficient GPU Communication Requires High-Speed Networking
The performance of large-scale AI systems is heavily dependent on the network infrastructure. Without fast and efficient communication between GPUs, model training can slow down. Meta’s largest AI jobs span thousands of GPUs, so all these units must interact seamlessly to perform parallelised computations.
The challenges are also increased by the complexity of LLMs which require extremely high bandwidth and low-latency network infrastructures. Without these characteristics, synchronisation between GPU nodes can become inefficient, effectively bottlenecking training performance.
Hardware Recovery Must Be Fast and Minimise Training State Loss
Given the constant risk of failure, the ability to quickly recover hardware and minimise training state loss was critical to Meta’s success. If a GPU fails during a large-scale AI job, losing valuable training data could render the entire operation useless. Therefore, Meta needed highly automated and fast recovery mechanisms that preserved the model's state, allowing it to resume training without significant loss.
Storage and Retrieval of Massive Datasets Must Remain Efficient
LLMs rely on access to massive datasets. With training requiring datasets often measured in petabytes, the ability to store and retrieve this data became a top priority. Meta could not afford latency or data retrieval slowdowns. Ensuring that the storage solutions they used were both scalable and high-speed was crucial for effective model training
Meta’s Solutions to Train AI at Scale
Meta used innovative hardware, network, scheduling and storage solutions to tackle these challenges. By optimising the architecture across the full AI stack, Meta was able to scale their operations while tackling potential failure points at multiple layers.
Hardware Innovations
The primary hardware innovation that Meta used was the development of the Grand Teton platform. To meet the computational demands of training large language models, Meta usedNVIDIA H100 GPUs with High Bandwidth Memory (HBM3), with an increased Thermal Design Power (TDP) of 700W to push the limits of GPU performance.
To ensure the system would fit within Meta’s constraints, Meta modified the cooling infrastructure, adapting the system to work within existing air-cooled environments. Given that making major adjustments to the cooling system would have been a significant and time-consuming task, the mechanical and thermal designs were adjusted to accommodate the changes.
Network Optimisation
Meta’s network strategy focused on ensuring that the colossal amount of data could move quickly and effectively between GPUs. Having been previously accustomed to RoCE (RDMA over Converged Ethernet) and InfiniBand fabrics, Meta built two 24k GPU clusters for their GenAI workloads. One of these clusters was optimised for RoCE, and the other for InfiniBand.
By using two different network fabrics in parallel, Meta was able to evaluate and compare their operational benefits, improving their knowledge base on networking and scalability for future deployments. The InfiniBand clusters offered the benefits of full-bisection bandwidth, while the RoCE cluster allowed for faster build times.
A crucial part of Meta’s optimisation process was configuring network communication patterns that addressed different layers of model, data, and pipeline parallelisms. This network topology was optimised so that it took advantage of each fabric’s specific capabilities, thus minimising latency and increasing communication speed.
Scheduling and Recovery Mechanisms
Meta tackled the issue of scheduling and failure recovery using dynamic scheduling techniques to ensure resources were allocated efficiently across diverse workloads. By creating these scheduling algorithms, Meta was able to automatically adapt resource allocation based on job priority, workload types, and failure likelihood.
Meta’s recovery strategies also aimed at minimising overhead time and quickly resuming the model training process following failures. For example, checkpointing the training data at regular intervals meant that, in the event of failure, the training state could be quickly restored from the point of failure, minimising overall losses.
Storage
With the massive volumes of data required for LLM training, storage performance played a major role in Meta’s ability to scale. Meta invested in high-speed, high-capacity storage solutions to handle large datasets. These technologies ensured both fast data retrieval and retention, making it possible to store large volumes of training data while also keeping retrieval times at a minimum.
What Enterprises Can Learn from Meta
From Meta’s experience, there are several takeaways for enterprises looking to scale their own AI infrastructures.
Invest in Reliable and High-Performance Hardware
The foundation of any large-scale AI infrastructure is reliable and high-performance hardware. Enterprises looking to scale must consider using cutting-edge GPUs and ensure the components can withstand continuous heavy use.
Optimise Network Topology
As workloads grow, efficient GPU communication becomes imperative. Enterprises should consider adopting fast networking technologies like InfiniBand to optimise their network topology to accommodate their specific requirements.
Implement Robust Recovery Mechanisms
Disruptions are inevitable, but minimising their impact can be achieved by implementing robust failure recovery mechanisms. Enterprises should build dynamic scheduling and fast checkpointing to maintain progress and minimise training downtime.
Build Scalable Infrastructure
Enterprises scaling AI workloads should ensure they have a scalable infrastructure that can handle growing computation needs and data management. Flexibility in infrastructure supports the continuous evolution of AI models and training workflows.
AI Supercloud for Scaling AI Training
Are you an enterprise looking to scale your AI training efforts? Don’t look further, choose the AI Supercloud to accelerate and scale your AI capabilities with our cutting-edge infrastructure and solutions. AI Supercloud is built with the unique demands of enterprises and large-scale GenAI applications in mind.
Here's how AI Supercloud helps enterprises handle the scaling challenges of AI training:
- Scalable Solutions: Enterprises can quickly access additional GPU resources on-demand for additional workload bursting via our cloud GPUaas Platform Hyperstack and scale to thousands of GPUs within as little as 8 weeks on the AI Supercloud.
- Latest NVIDIA GPUs: At AI Supercloud, we offer powerful NVIDIA hardware like the NVIDIA HGX H100, NVIDIA HGX H200 and the NVIDIA Blackwell GB200 NVL72/36 for high-throughput AI tasks.
- Optimised Hardware: We don’t just offer hardware but optimise it with innovative technologies such as advanced liquid cooling, low-latency networking and NVIDIA Quantum-2 InfiniBand to ensure high efficiency, low latency and maximum performance when training or deploying large-scale AI models. We also offer NVIDIA-certified WEKA storage with GPUDirect Storage support to ensure fast data retrieval and retention.
- Managed Services: Our AI Supercloud platform includes fully managed Kubernetes for seamless orchestration of your AI workloads, paired with MLOps tools to ensure that your model lifecycle—from development to deployment—runs smoothly and securely at scale.
- Secure Platform: Data sovereignty and sustainability are imperative at the AI Supercloud. We’ve built our infrastructure with a commitment to these values with data centres in Europe and Canada to support your GenAI needs while ensuring compliance with regional regulations and reducing environmental impact.
Take the first step toward scaling your AI infrastructure efficiently. Book a call with our solutions engineer to explore how AI Supercloud can scale your GenAI initiatives.
Similar Reads
- How to Scale LLMs with the AI Supercloud
- How AI Supercloud Accelerates Large AI Model Training
- GPU Clusters for AI: Scalable Solutions for Growing Business
- Enterprise Challenges in AI Adoption and How to Overcome Them
FAQs
How did Meta address GPU failures in their AI infrastructure?
Meta implemented automated recovery mechanisms and redundancy plans to quickly address hardware failures, minimising training disruptions.
What network strategies did Meta use for scaling AI training?
Meta used a combination of RoCE and InfiniBand network fabrics to ensure high-speed communication and low-latency data transfer between GPUs.
How does the AI Supercloud ensure security and compliance for enterprises?
The AI Supercloud’s infrastructure is built with a strong focus on data sovereignty, offering data centres in Europe and Canada to ensure regional regulatory compliance.
Why should enterprises choose the AI Supercloud for their GenAI applications?
The AI Supercloud provides high-performance, flexible, and secure infrastructure tailored for large-scale GenAI workloads, ensuring maximum efficiency and scalability.
What makes AI Supercloud ideal for large-scale AI training?
The AI Supercloud’s cutting-edge GPUs managed Kubernetes, and optimised networking technologies provide the perfect environment for handling demanding AI workloads at scale.
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