LASER tracks low-rank activation subspaces in recursive models via matrix-free SVD updates and fidelity resets to save 60% memory without accuracy loss.
Mahoney, and Joseph E
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TACO compresses tensor-parallel intermediate tensors with an adaptive FP8 scheme and fused kernels, yielding up to 1.87X throughput gains on GPT and Qwen models with near-lossless accuracy.
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LASER: Low-Rank Activation SVD for Efficient Recursion
LASER tracks low-rank activation subspaces in recursive models via matrix-free SVD updates and fidelity resets to save 60% memory without accuracy loss.
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TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training
TACO compresses tensor-parallel intermediate tensors with an adaptive FP8 scheme and fused kernels, yielding up to 1.87X throughput gains on GPT and Qwen models with near-lossless accuracy.