11 papers · 1 filter
KV Cache Offloading for Context-Intensive Tasks
Andrey Bocharnikov, Ivan Ermakov, Denis Kuznedelev +2
With the growing demand for long-context LLMs across a wide range of applications, the key-value (KV) cache has become a critical bottleneck for both latency and memory usage. Rece…
Asynchronous Reasoning: Training-Free Interactive Thinking LLMs
George Yakushev, Nataliia Babina, Masoud Vahid Dastgerdi +4
Many state-of-the-art LLMs are trained to think before giving their answer. Reasoning can greatly improve language model capabilities, but it also makes them less interactive: give…
Bridging the Gap Between Promise and Performance for Microscaling FP4 Quantization
Vage Egiazarian, Roberto L. Castro, Denis Kuznedelev +8
The recent hardware-accelerated microscaling 4-bit floating-point formats such as MXFP4 and NVFP4, supported on NVIDIA and AMD GPUs, promise to revolutionize large language model (…
Hogwild! Inference: Parallel LLM Generation via Concurrent Attention
Gleb Rodionov, Roman Garipov, Alina Shutova +6
Large Language Models (LLMs) have demonstrated the ability to tackle increasingly complex tasks through advanced reasoning, long-form content generation, and tool use. Solving thes…
EvoPress: Accurate Dynamic Model Compression via Evolutionary Search
Oliver Sieberling, Denis Kuznedelev, Eldar Kurtic +1
The high computational costs of large language models (LLMs) have led to a flurry of research on LLM compression, via methods such as quantization, sparsification, or structured pr…
Cache Me If You Must: Adaptive Key-Value Quantization for Large Language Models
Alina Shutova, Vladimir Malinovskii, Vage Egiazarian +5
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 computa…