Bringing PyTorch Monarch to AMD GPUs
Featured projects Training state-of-the-art large language models (LLMs) with billions of parameters requires distributed training across hundreds or thousands of GPUs. At this scale, hardware failures are not exceptional events—they are expected. A single GPU memory error, network partition, or node crash can bring down an entire training run that has been progressing for days or weeks.
- ▪Featured projects Training state-of-the-art large language models (LLMs) with billions of parameters requires distributed training across hundreds or thousands of GPUs.
- ▪At this scale, hardware failures are not exceptional events—they are expected.
- ▪A single GPU memory error, network partition, or node crash can bring down an entire training run that has been progressing for days or weeks.
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Featured projects Training state-of-the-art large language models (LLMs) with billions of parameters requires distributed training across hundreds or thousands of GPUs. At this scale, hardware failures are not exceptional events—they are expected. A single GPU memory error, network partition, or node crash can bring down an entire training run that has been progressing for days or weeks. While our previous work demonstrated near-linear scaling of FP8 training at scale (achieving 96.16% scaling efficiency on a 1024-GPU MI325 cluster with DeepSeekV3-671B), the key challenge remains: reliability at scale.
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