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Peking University, PKU
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Sub-2-bit LLMs can now achieve state-of-the-art performance thanks to pQuant, which selectively preserves sensitive parameters in a high-precision branch during quantization-aware training.
Multi-round LLM inference gets a major speed boost with AMPD, a new disaggregated serving framework that intelligently manages interleaved prefill-decode workloads.
