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AMD upstreamed optimizations to PyTorch/TorchTitan and TorchAO for FP8 training on AMD Instinct GPUs, achieving up to 13.4% throughput gains on Llama3-8B and recovering 89% of FP8 quantization overhead on DeepSeek-V3 via fused Triton kernels.
SKT 在 Hugging Face 上发布了 A.X K2，一个 688B 参数的稀疏 MoE 语言模型，拥有 33B 激活参数，原生采用 FP8 训练，并具备 Think/Non-Think 推理模式。