Generative AI can transform many business operations, automating tasks such as text summarization, translation, insight prediction, and content generation. However, the journey to fully integrating this technology has been filled with challenges. Running a powerful generative AI model like ChatGPT-4 may require tens of thousands of GPUs. Each inference instance for large language models like ChatGPT incurs significant costs, and they become even higher for state-of-the-art vision generation models like OpenAI’s Sora.
That’s where AMD steps in, offering powerful solutions to help businesses unlock the potential of generative AI. AMD has gone all-in on generative AI, focusing on data center GPU products like the AMD Instinct™ MI300X accelerator, open software such as ROCm™ and developing a collaborative software ecosystem.
Effective generative AI needs high-performance hardware solutions
AMD isn’t just keeping up with the competition. We’re raising the bar, enabling more companies to push the limits of what’s possible with AI. The AMD MI300X accelerator stands out with its leading inferencing speed and massive memory capacity, which are crucial for efficiently managing the heavy lifting required by generative AI models.
Along with fast inference speeds, the AMD Instinct™ MI300X accelerator offers up to 5.3 TB/s of peak theoretical memory bandwidth, significantly surpassing the 4.9 TB/s of the Nvidia H200(1). With 192 GB of HBM3 memory, the MI300X can support the Llama3 model with 8 billion parameters on a single GPU, eliminating the need to split this model across multiple GPUs. Its large memory means the AMD MI300X can handle extensive datasets and complex models. This allows for management of larger batch sizes without memory errors, which can translate to faster, more efficient real-time AI applications.
A software ecosystem built to overcome challenges
In the recent past, adopting generative AI was particularly challenging for enterprises accustomed to NVIDIA’s CUDA® ecosystem. To expand accelerator options for the marketplace, AMD has invested heavily in software development to maximize the compatibility of the AMD ROCm software ecosystem with CUDA. Through collaborations with open-source frameworks like Megatron, DeepSpeed, and others, AMD has led a concerted effort toward bridging the gap between CUDA and ROCm, making transitions smoother for developers.
Collaborations with industry leaders has further integrated the ROCm software stack into popular AI templates and deep learning frameworks. This ongoing investment makes for an increasingly smooth transition converting from CUDA to ROCm. Hugging Face, the largest library for open-source models, is also a significant partner to AMD. We help to ensure that almost all Hugging Face models run on AMD Instinct accelerators without modification, simplifying the process for developers to perform inference or fine-tuning.
Since approximately 90% of models trained or submitted to Hugging Face are in PyTorch, our close collaboration with the PyTorch Foundation means that new PyTorch versions are thoroughly tested on AMD hardware, leading to significant performance optimizations like Torch Compile and PyTorch-based quantization.
Collaboration with the developers of JAX, a critical AI framework developed by Google, makes it easier to compile ROCm software-compatible versions of JAX and related frameworks like JAX Lab and Flax. This is crucial for enterprises developing custom GenAI models with enhanced training and inference speeds.
Databricks’ success with AMD Instinct MI250 GPUs in training large language models (LLMs) highlights the impressive capabilities of AMD hardware. Leveraging technologies like ROCm and FlashAttention-2, Databricks noted significant performance improvements, demonstrating near-linear scaling and efficiency in multi-node configurations. This collaboration showcases the ability of AMD accelerators to handle demanding AI workloads effectively, offering powerful and cost-effective solutions for enterprises venturing into generative AI.
These collaborations are particularly beneficial for smaller customers who might not have the resources to purchase premium-priced GPUs. Working together with these providers allows them to offer customers AMD accelerator services as a cloud service.
Efficient scaling with 3D parallelism techniques
AMD uses advanced 3D parallelism techniques to enhance the training of large-scale generative AI models, helping ensure efficiency and effectiveness. Data parallelism splits vast datasets across different GPUs, processing terabytes of data efficiently and preventing bottlenecks. Tensor parallelism distributes very large models at the tensor level across multiple GPUs, balancing the workload and speeding up complex model processing. Pipeline parallelism distributes layers of models like transformers across several GPUs, enabling simultaneous processing and significantly accelerating the training process. Fully supported within ROCm, these techniques allow customers to easily handle extremely large models. The Allen AI Institute utilized a network of AMD Instinct MI250 Accelerators, employing these parallelism techniques to train their OLMo model.
Dedicated Support at every step along your generative AI journey
AMD simplifies the development and deployment of GenAI models by using microservices. These microservices support common data workflows, facilitating data processing and model training automation. They can ensure data pipelines run smoothly, allowing customers to focus on model development.
Ultimately, what sets AMD apart from its competitors is its demonstrated commitment to all its customers, regardless of their size. This level of attention is particularly beneficial for enterprises application partners that may need more resources to navigate complex AI deployments on their own.
References
Generative AI – tech blogs
Posts tagged GenAI — ROCm Blogs (amd.com)
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MI300x Claims - https://www.amd.com/en/products/accelerators/instinct/mi300/mi300x.html
(1) Calculations conducted by AMD Performance Labs as of November 17, 2023, for the AMD Instinct™ MI300X OAM accelerator 750W (192 GB HBM3) designed with AMD CDNA™ 3 5nm FinFet process technology resulted in 192 GB HBM3 memory capacity and 5.325 TFLOPS peak theoretical memory bandwidth performance. MI300X memory bus interface is 8,192 and memory data rate is 5.2 Gbps for total peak memory bandwidth of 5.325 TB/s (8,192 bits memory bus interface * 5.2 Gbps memory data rate/8).
The highest published results on the NVidia Hopper H200 (141GB) SXM GPU accelerator resulted in 141GB HBM3e memory capacity and 4.8 TB/s GPU memory bandwidth performance.
https://nvdam.widen.net/s/nb5zzzsjdf/hpc-datasheet-sc23-h200-datasheet-3002446
The highest published results on the NVidia Hopper H100 (80GB) SXM5 GPU accelerator resulted in 80GB HBM3 memory capacity and 3.35 TB/s GPU memory bandwidth performance.
https://resources.nvidia.com/en-us-tensor-core/nvidia-tensor-core-gpu-datasheet
