5 papers
Leech Lattice Vector Quantization for Efficient LLM Compression
Tycho F. A. van der Ouderaa, Mart van Baalen, Paul Whatmough +1
Scalar quantization of large language models (LLMs) is fundamentally limited by information-theoretic bounds. While vector quantization (VQ) overcomes these limits by encoding bloc…
Mixture of Cache-Conditional Experts for Efficient Mobile Device Inference
Andrii Skliar, Ties van Rozendaal, Romain Lepert +5
Mixture of Experts (MoE) LLMs have recently gained attention for their ability to enhance performance by selectively engaging specialized subnetworks or "experts" for each input. H…
GPTVQ: The Blessing of Dimensionality for LLM Quantization
Mart van Baalen, Andrey Kuzmin, Ivan Koryakovskiy +6
In this work we show that the size versus accuracy trade-off of neural network quantization can be significantly improved by increasing the quantization dimensionality. We propose…
Efficient LLM Inference using Dynamic Input Pruning and Cache-Aware Masking
Marco Federici, Davide Belli, Mart van Baalen +5
While mobile devices provide ever more compute power, improvements in DRAM bandwidth are much slower. This is unfortunate for large language model (LLM) token generation, which is…
Sparse High Rank Adapters
Kartikeya Bhardwaj, Nilesh Prasad Pandey, Sweta Priyadarshi +9
Low Rank Adaptation (LoRA) has gained massive attention in the recent generative AI research. One of the main advantages of LoRA is its ability to be fused with pretrained models,…