Large language models (LLMs) may seem ubiquitous and accessible, yet behind the scenes, there is intense competition for the GPU resources needed to power them. Cost, availability, and performance constraints create significant barriers for those looking to develop and deploy LLMs and their visual counterparts. These models rely on billions of parameters being processed simultaneously, creating substantial computational and memory demands. The massive scale that enables their remarkable capabilities also present challenges to deploy cost-effectively. AI inferencing, where trained models generate and deliver predictions or outputs, can be quite compute intensive which also brings TCO challenges. However, the AMD Instinct™ MI300X accelerator helps to overcome these barriers and realize the potential of LLMs.
Substantial memory bandwidth and capacity to support larger models
High bandwidth is crucial for handling the large datasets and computations LLMs demand, enabling faster processing, reduced latency, and better overall performance. The AMD MI300X accelerator offers up to 5.3 TB/s of peak memory bandwidth, significantly surpassing the 4.9 TB/s of the Nvidia H200. With 192 GB of HBM3 memory, the MI300X can support models with up to 80 billion parameters on a single GPU, eliminating the need to split models of this size across multiple GPUs. In contrast, the Nvidia H200, with 141 GB of HBM2e memory, may require splitting models, leading to complexities and inefficiencies in data transfer.
The large memory capacity of the AMD MI300X GPU allows for more of the model to be stored closer to the compute units, helping reduce latency and improve performance. Moreover, the substantial memory capacity of the MI300X enables it to handle many large models on a single GPU, addressing the challenge of splitting these models across GPUs and the associated execution complexities that comes with this action. The MI300X simplifies deployment and enhances performance by minimizing potential inefficiencies in data transfer, making the it an excellent choice for managing the demanding requirements of LLMs.
The MI300X GPU’s combination of large memory capacity and high bandwidth means it can perform tasks on a single GPU that would require multiple GPUs with the H200. This can simplify deployment and cut costs. These capabilities can also reduce the complexity of managing multiple GPUs and improve throughput. Running a model like ChatGPT on MI300X potentially needs fewer GPUs than on a H200. making it a a great option for enterprises aiming to deploy advanced AI models.
Enhancing LLM inference with Flash Attention
AMD GPUs such as MI300X support Flash Attention, a crucial advance in optimizing LLM inference on GPUs. Traditional attention mechanisms involve multiple reads and writes to high-bandwidth memory (HBM), leading to bottlenecks. Flash Attention addresses this by combining operations such as activation and dropout into a single step, reducing data movement and thus increasing speed. This optimization is particularly beneficial for LLMs, allowing faster and more efficient processing. (Click here for more on Flash Attention.)
Floating point operations performance
Performance in floating point operations is an important metric for LLM performance. The MI300X delivers up to 1.3 PFLOPS of FP16 (half-precision floating point) performance and 163.4 TFLOPS of FP32 (single-precision floating point). These performance levels help ensure that complex computations involved in LLMs run efficiently and accurately. This performance is also significant for tasks that require intense numerical calculations, such as matrix multiplications and tensor operations, which are foundational to deep-learning models.
The architecture of the MI300X supports advanced parallelism, enabling it to process multiple operations simultaneously. With 304 compute units, the MI300X can efficiently handle the vast number of parameters in LLMs, enabling it to perform complex tasks effectively.
An optimized open software stack for porting and building LLMs
The AMD ROCm™ software platform provides an open and robust foundation for AI and HPC workloads. ROCm offers libraries, tools, and frameworks tailored for AI, helping to ensure that AI developers can readily utilize the MI300X GPU’s capabilities. ROCm allows developers to seamlessly port code developed on CUDA to ROCm with minimal changes, helping ensure compatibility and efficiency.
Upstream ROCm software support of leading AI frameworks, such as PyTorch or TensorFlow, allows for thousands of Hugging Face and other LLMs to run out of the box. It also facilitates integratesn frameworks such as PyTorch and libraries such as Hugging Face with AMD GPUs, creating a straightforward path for integration of LLMs on MI300X. This integration ensures that developers can maximize the performance of their applications and deliver peak performance for LLM inference when using AMD GPUs.
Delivering real-world impact
AMD collaborates across an open ecosystem with industry partners like Microsoft, Hugging Face, and the OpenAI Triton team to optimize LLM inference models and tackle real-world challenges. The Microsoft Azure cloud platform uses AMD GPUs, including the MI300X, to enhance enterprise AI services. Another notable deployment of the MI300X by Microsoft and OpenAI is with ChatGPT-4, showcasing the capability of AMD GPUs to handle large-scale AI workloads efficiently. Hugging Face leverages AMD hardware to fine-tune models and improve inference speeds, while collaboration with the OpenAI Triton team focuses on integrating advanced tools and frameworks.
In summary, the AMD Instinct MI300X accelerator is a strong choice for deploying large language models due to its ability to address cost, performance, and availability challenges. By providing a reliable ,efficient alternative, and a strong ROCm ecosystem AMD helps businesses maintain robust AI operations and achieve optimal performance.
Please find additional resources on AMD Instinct and ROCm below:
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LLM Optimization Tech Blog
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https://rocm.blogs.amd.com/artificial-intelligence/llm-inference-optimize/README.html
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Huggingface MI300X Blog
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https://huggingface.co/blog/huggingface-amd-mi300
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ROCm AI LLM docs
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https://rocm.docs.amd.com/en/latest/how-to/llm-fine-tuning-optimization/index.html
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SmoothQuant model inference on AMD Instinct MI300X Tech Blog
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Accelerating Large Language Models with Flash Attention Tech Blog
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Accelerating Large Language Models with Flash Attention on AMD GPUs — ROCm Blogs
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Step-by-Step Guide to Use OpenLLM Tech Blog
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Step-by-Step Guide to Use OpenLLM on AMD GPUs — ROCm Blogs
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Configuration details:
MI300x Claims - https://www.amd.com/en/products/accelerators/instinct/mi300/mi300x.html
