Efficient large language model (LLM) inference depends critically on how requests are scheduled and batched relative to the key-value (KV) cache state across GPU memory. Traditional scheduling strategies — round-robin, least-loaded, and even continuous batching — treat the KV cache as a passive byproduct of inference rather than an active scheduling constraint. This article investigates three r...
Large language model inference comprises two computationally distinct phases — prefill and decode — that exhibit fundamentally different hardware utilization profiles. Colocating both phases on the same GPU leads to resource contention and suboptimal utilization, a problem that disaggregated architectures address by separating prefill and decode onto dedicated hardware pools. This article inves...
As large language models scale beyond the memory capacity of individual accelerators, distributing inference across multiple GPUs introduces fundamental challenges for key-value cache management. This article examines how tensor parallelism, pipeline parallelism, and emerging hybrid strategies partition KV-cache state across devices, analyzing the communication overhead, memory efficiency, and ...
Flash Attention has become the foundational kernel technology enabling memory-efficient inference in large language models (LLMs), transforming how attention computation interacts with GPU memory hierarchies. This article investigates three research questions: (1) how does Flash Attention's tiling strategy reduce peak memory consumption compared to standard attention, and what are the theoretic...
As large language models (LLMs) scale to context windows exceeding one million tokens, the key-value (KV) cache grows linearly and becomes the dominant memory bottleneck during autoregressive inference. Sliding window attention and compressive caching represent two complementary families of techniques that bound memory usage while preserving access to long-range context. This article investigat...
As large language models (LLMs) scale to billions of parameters and context windows stretch beyond 128K tokens, the key-value (KV) cache becomes the dominant memory bottleneck during inference. Cross-layer KV-cache sharing represents a family of techniques that exploit redundancy in key and value representations across transformer layers to reduce cache memory without retraining. This article i...
The value-added tax (VAT) compliance gap represents the difference between theoretical VAT liability and actual VAT revenue collected, serving as a primary quantitative indicator of tax evasion and shadow economic activity. This article examines the methodological landscape for VAT gap estimation, applies a comparative framework to Ukraine's fiscal context, and benchmarks Ukrainian VAT performa...
Pharmaceutical portfolio management operates under persistent uncertainty from supply chain disruptions, regulatory shifts, and demand volatility. While the HPF-P framework provides Decision Readiness Index (DRI) and Decision Readiness Level (DRL) metrics for portfolio assessment, their behavior under extreme stress conditions remains uncharacterized. This article develops a multi-scenario stre...
This article investigates token pruning and attention sparsity as complementary strategies for reducing KV-cache memory consumption during large language model inference. Building on our series analysis of semantic prompt caching, we examine how selective token removal and sparse attention patterns can achieve 50-80% memory reduction while preserving generation quality. Three research questions...
Open-source benchmark frameworks have become the backbone of AI model evaluation, yet none provides simultaneous coverage of multidimensional intelligence measurement, inference cost normalization, and cryptographic reproducibility certification. This article presents the architecture and design rationale for the Universal Intelligence Benchmark (UIB) open-source suite, a modular evaluation fra...