Calculate GPU Requirements for Your LLM Training

Graphics Processing Unit (GPU)

GPUs are a cornerstone of LLM training due to their ability to accelerate parallel computations. Modern deep learning frameworks, such as TensorFlow and PyTorch, leverage GPUs to perform matrix multiplications and other operations required for neural network training. When selecting a GPU, factors like memory capacity (VRAM), memory bandwidth, and processing power (measured in CUDA cores) are crucial. High-end GPUs like NVIDIA’s Tesla series or the GeForce RTX series are commonly favored for LLM training. The more powerful the GPU, the faster the training process.

Data Center GPU Options

The following are some of the world’s most powerful, data center grade GPUs, commonly used to build large-scale GPU infrastructure.

NVIDIA Tesla A100

The A100 is based on Tensor Cores and leverages multi-instance GPU (MIG) technology. It is built for workloads such as high-performance computing (HPC), machine learning and data analytics.

Tesla A100 is intended for scalability (up to thousands of units) and can be separated into seven GPU instances for different workload sizes. The A100 offers performance reaching up to 624 teraflops (billion floating-point operations per second) and has 40GB memory, 1,555 GB bandwidth and 600GB/s interconnects.

NVIDIA Tesla V100

The V100 GPU is also based on Tensor Cores and is designed for applications such as machine learning, deep learning and HPC. It uses NVIDIA Volta technology to accelerate common tensor operations in deep learning workloads. The Tesla V100 offers performance reaching 149 teraflops as well as 32GB memory and a 4,096-bit memory bus.

NVIDIA Tesla P100

The Tesla P100 GPU is based on an NVIDIA Pascal architecture designed specifically for HPC and machine learning. P100 offers performance of up to 21 teraflops, with 16GB of memory and a 4,096-bit memory bus.

NVIDIA Tesla K80

The K80 GPU uses NVIDIA Kepler architecture, which enables the accelerating of data analytics and scientific computing. It incorporates GPU Boost™ technology and 4,992 NVIDIA CUDA cores. Tesla K80 offers up to 8.73 teraflops performance, with 480GB memory bandwidth and 24GB of GDDR5 memory.

Google TPU

Google offers slightly different tensor processing units (TPUs), which are application-specific integrated circuits (ASICs) based on chips or the cloud, that support deep learning. These TPUs are designed specifically to be used with TensorFlow and can only be found on the Google Cloud Platform.

Google TPUs offer performance of up to 420 teraflops with a high bandwidth memory (HBM) of 128 GB. You can also find pod versions that offer performance of over 100 petaflops with 32TB HBM and 2D toroidal mesh networks.

Commodity GPUs only have 16 GB / 24 GB GPU memory, and even the most advanced NVIDIA A100 and V100 GPUs only have 40 GB / 80 GB of GPU memory per device.

How to calculate no of A100 GPU needed for LLM Training?

1. No of token in billions

2. Hour to finish training

3. Enter model size in GB

4. No of epochs

Number of A100 (80G) GPUs needed for Training= ((tokens * epochs * model_size * 13.3) / hours)

How to calculate no of A100 GPU needed for LLM Inference?

1. Model throughput (in tokens/sec)

2. Max number queries per minute

3. Average number of output tokens

Number of A100 (80G) GPUs needed for Inference= (output_tokens / throughput * qpm / 60)

Model Memory Calculator

This tool will help you calculate how much vRAM is needed to train and perform big model inference on a model hosted on the 🤗 Hugging Face Hub.

https://huggingface.co/docs/accelerate/main/en/usage_guides/model_size_estimator

Different GPU performance comparison

1. https://developer.nvidia.com/hpc-application-performance

2. https://lambdalabs.com/gpu-benchmarks

3. https://huggingface.co/docs/transformers/perf_train_gpu_one#anatomy-of-models-memory

Best Practices for Effective Distributed LLM Training

Here are the best practices for implementing effective distributed systems in LLM training:

1.     Choose the Right Framework: Utilize frameworks designed for distributed training, such as TensorFlow or PyTorch. These frameworks provide tools and APIs that simplify the implementation of distributed training strategies.

2.     Optimize Communication: Minimize communication overhead by using techniques like gradient accumulation before updating the model or using techniques like gradient compression to reduce the amount of data exchanged between nodes.

3.     Experiment with Batch Sizes: Finding the optimal batch size for distributed training is crucial. Too small a batch size might lead to increased communication overhead, while too large a batch size can cause memory constraints.

4.     Monitor and Tune: Regularly monitor the performance of your distributed training setup. Adjust hyperparameters, partitioning strategies, and communication settings to optimize performance.

5.     Backup and Recovery: Implement mechanisms for regular model checkpoints and efficient recovery in case of failures. This ensures that training can be resumed without starting from scratch.

Challenges of Distributed LLM Training

While distributed systems offer significant advantages for speeding up LLM training, they also introduce challenges that must be addressed:

• Communication Overhead: In distributed systems, communication between nodes becomes a potential bottleneck. When aggregating gradients or exchanging model updates, the time spent on communication can impact the overall speedup.

• Synchronization Complexity: Coordinating the updates from multiple machines can be complex, especially in model parallelism scenarios. Ensuring that different parts of the model are synchronized correctly requires careful design.

• Failure Handling: Distributed systems introduce the possibility of individual nodes failing. Robust mechanisms for handling failures and resuming training are essential to maintain progress.

• Resource Management: Efficiently managing resources across multiple machines, including CPUs and GPUs, requires sophisticated resource allocation and scheduling strategies.

References

1. https://docs.nvidia.com/deeplearning/performance/dl-performance-gpu-background/index.html

2. https://medium.com/@dzmitrybahdanau/the-flops-calculus-of-language-model-training-3b19c1f025e4

3. https://huggingface.co/docs/accelerate/main/en/concept_guides/training_tpu

4. https://towardsdatascience.com/how-to-build-a-multi-gpu-system-for-deep-learning-in-2023-e5bbb905d935

5. https://blog.eleuther.ai/transformer-math/

6. https://timdettmers.com/2023/01/30/which-gpu-for-deep-learning/#When_do_I_need_11_GB_of_Memory

7. https://lambdalabs.com/gpu-benchmarks