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Vladimir Malinovskii

3 accepted papers

2025

Cache Me If You Must: Adaptive Key-Value Quantization for Large Language Models

ICML 2025poster

Efficient real-world deployments of large language models (LLMs) rely on Key-Value (KV) caching for processing and generating long outputs, reducing the need for repetitive computation. For large contexts, Key-Value caches can take up tens of gigabytes of device memory, as they store vector represen…

2025

HIGGS: Pushing the Limits of Large Language Model Quantization via the Linearity Theorem

NAACL 2025long

Quantizing large language models has become a standard way to reduce their memory and computational costs. Typically, existing methods focus on breaking down the problem into individual layer-wise sub-problems, and minimizing per-layer error, measured via various metrics. Yet, this approach currentl…

Cited by 6SourcePDFScholar
2024

PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression

NeurIPS 2024oral

There has been significant interest in "extreme" compression of large language models (LLMs), i.e. to 1-2 bits per parameter, which allows such models to be executed efficiently on resource-constrained devices. Existing work focused on improved one-shot quantization techniques and weight represent…