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A new approach, called Q-PACE, consisting of a second-order sensitivity model that predicts the loss increase as a sum of quantization noise MSE weighted by per-layer curvature coefficients, and its stability during training allows for infrequent, cheap recalibration.
This work presents an end-to-end hardware-software co-design framework that compresses expert weights into hardware-native, low-precision sparse representations and accelerates their execution on SpTCs, establishing hardware-software co-design as a practical path toward scalable and efficient MoE deployment.
This work introduces WUSH-KV, which adapts WUSH, which constructs a data-aware transform from the second-order statistics of both factors in a matrix product to reduce quantization error and achieves the lowest end-to-end perplexity among other tested transforms.
Riemannian Constrained Optimization (RCO) is proposed, which wraps tangent projection, binary-search retraction, and momentum transport around a standard Adam step and optimizes the actual loss with first-order methods, enforces the expected budget exactly at every iterate, and introduces no constraint-related hyperpar...
Assessment of accuracy under disaggregated serving in vLLM and further validate shared-weight format disaggregation through post-training quantization on models up to 2.8T parameters are evaluated.
A family of difference-informed pruning methods built upon this principle are introduced, suggesting that preserving output differences is a broadly useful and composable signal for post-training LLM sparsification.
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