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Quantization-aware training (QAT) leverages lower-precision arithmetic to reduce the cost of LLM deployment, but aggressive quantization degrades final model performance. A common remedy is mixed-precision training, in which high precision is assigned to some of the layers to maintain performance while keeping the cost...
Mixture-of-Experts (MoE) architectures allow frontier language models to scale to trillions of parameters, but their deployment is constrained by massive memory footprints and memory-bandwidth limitations. Although modern accelerators provide Sparse Tensor Cores (SpTCs) that reduce weight storage and increase throughpu...
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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