10 Challenges and Solutions for Training Foundation LLMs

The latest Meta model Llama 3 not only broke performance records but also emphasised scalable and efficient computing solutions to handle its massive training demands. Meta’s most efficient implementation for Llama 3 achieves a compute utilisation of over 400 TFLOPS per GPU when trained on 16K GPUs simultaneously. They performed training runs on two custom-built 24K GPU clusters necessary for such high-performance hardware setups for effective large-scale AI model training.

Continue reading as we explore the challenges that organisations face while training foundation models. We will use the latest insights by Meta from their training of Llama 3 models.

5 Key Challenges and Solutions for Training Foundation LLMs

Here are the most common challenges along with solutions for training foundation LLMs: 

1. High Computational Demands

Training LLMs require immense computational power. Models with billions of parameters, such as Llama 3, necessitate extensive GPU or TPU clusters. For context, training GPT-3 with its 175 billion parameters, would take 288 years on a single NVIDIA V100 GPU. In practice, training is distributed across thousands of GPUs. Google's PaLM model was trained using 6,144 TPU v4 chips. This scale of computing infrastructure is crucial for handling the massive amounts of data and complex calculations involved. 

Choosing the right GPU is crucial to meeting these computational demands. At Hyperstack, we offer NVIDIA H100 SXM VMs optimised for AI workloads. The NVIDIA H100 SXM’s unparalleled compute power ensures efficient training for your foundation models. Want to find the perfect GPU for your needs? Check out our GPU Selector Tool to find the best GPU configuration for your fine-tuning and inference tasks.

2. Error Management and System Reliability

Despite advanced hardware, large-scale training risks errors and system failures. Without effective error recovery, training can be interrupted, wasting resources and increasing costs.

Meta addressed these challenges for Llama 3 by reducing job startup and checkpointing time and developing fast-diagnosis tools. For instance, they extensively used PyTorch’s built-in NCCL flight recorder, which captures communication metadata and stack traces, allowing for rapid issue diagnosis at scale, particularly with NCCLX (see here). This enabled the efficient recording of every communication event and collective operation duration, while automatically dumping tracing data on the NCCLX watchdog or heartbeat timeout. Additionally, they developed tools to identify stragglers, i.e. still-functioning hardware that is running slow.

Our hardware, equipped with preinstalled NVIDIA GPU drivers and libraries, offers a seamless experience for AI workloads, eliminating the usual setup friction. With GPUs ready to go out of the box, users can leverage PyTorch with NCCL, simplifying error handling and communication diagnostics.

3. Training Efficiency

Inefficiencies in training can lead to longer durations and higher costs. Foundation models often require optimised training stacks that manage memory, handle checkpoints and reduce rollback times to improve efficiency. At Hyperstack, we support training processes with our high-speed networking with up to 350Gbps which allows seamless communication between GPUs during distributed training. This can boost efficiency and reduce training times significantly. We also offer NVMe storage options for high-speed data read/write operations. This accelerates data processing and reduces latency during model checkpoints, thus preventing training rollbacks due to system bottlenecks. 

4. Resource Allocation and Cost

Training models like Llama 3 require substantial financial investments, particularly in high-performance GPUs. Inefficient resource allocation can lead to skyrocketing costs, making the training of foundation models unaffordable for many organisations.

Hyperstack offers NVIDIA H100 SXM and NVIDIA H100 PCIe GPUs, specifically designed to meet the needs of foundation LLM training. By offering long-term reservation options, you can secure these high-performance GPUs at cost-effective pricing that ensures that your AI projects remain scalable and budget-friendly. Check out our GPU pricing to see how you can optimise costs for your training workloads.

5. Environmental Impact

The vast amount of energy required for training foundation models raises concerns about their environmental impact. Large-scale training consumes significant power, contributing to carbon emissions.  We understand this and offer access to energy-efficient high-performance cloud GPUs for foundation LLM training, ensuring that your workloads are completed with minimal environmental footprint. We have also partnered with AQ Compute to deliver net zero emissions operations with the Carbon-Neutral Data Centre Pact (CNDCP). Learn more about partnership to reduce environmental impact with our cloud platform here. 

Conclusion 

Training foundation LLMs comes with its own set of challenges but with the right infrastructure and optimisations, you can overcome these hurdles. By leveraging our high-performance and energy-efficient solutions, Hyperstack enables faster, more reliable and more cost-effective AI model training.

Sign up now to start training your foundation models with Hyperstack today!

FAQs

What is foundational LLM training?

Foundation LLM training involves developing AI models capable of understanding and generating human language, trained on massive amounts of data.

What factors should be considered when training foundation models?

Key factors include choosing the right computational infrastructure, managing costs, ensuring data quality and addressing environmental concerns.

Which is the best GPU for foundation LLM training?

We recommend the NVIDIA H100 SXM and NVIDIA H100 PCIe, as they are specifically optimised for LLM workloads.