Instinct Accelerators - Page 3

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Instinct Accelerators - Page 3


From high-performance computing, deep-learning, and rendering systems, to cloud computing, training complex neural networks, and AMD’s ROCm open ecosystem these blogs offer more insights and updates into our products and solutions.


AMD Instinct™ Accelerators Expand Reach in HPC and AI Workloads

Carol_Whitmarsh
Staff
Staff
‎01-31-2024 01:56 PM

AMD continues to cornerstone the technology needed to enable the computational capabilities and possibilities of the world’s most powerful supercomputers across various sectors.

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Competitive performance claims and industry leading Inference performance on AMD Instinct MI300X

Anonymous
Not applicable
‎12-15-2023 12:55 PM

We are at a stage in our product ramp where we are consistently identifying new paths to unlock performance with our ROCM software and AMD Instinct MI300 accelerators. We have made a lot of progress since we recorded data in November that we used at our launch event and are delighted to share our latest results highlighting these gains.

These gains show that AMD Instinct MI300X with ROCm 6 continues to show leadership inference performance using the popular FP16 datatype and vLLM inference library compared to Nvidia H100 using TensorRT-LLM and FP16 or FP8 datatypes.

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Introducing the AMD Instinct™ MI300 Series accelerators, Powering the Growth of AI and HPC at Scale

guy_ludden
Staff
Staff
‎12-06-2023 04:32 PM

The newest family of AMD accelerators, the AMD Instinct™ MI300 Series, featuring the third-generation Compute DNA (AMD CDNA™ 3) architecture, offer two distinct variants designed to address AI and HPC markets.

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Application-Transparent Emulation of TensorFloat32^1 using BFloat16 on AMD GPUs

Tyler_Hofstede
Staff
Staff
‎11-01-2023 03:35 PM

Most Machine Learning (ML) engineers use single precision (FP32) datatype for developing ML models. TensorFloat32 (TF32) has recently become popular as a drop-in replacement for these FP32 based models. However, there is a pressing need to provide additional performance gains for these models by using faster datatypes (such as BFloat16 (BF16)) without requiring additional code changes.

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