Category: AI Memory

Multi-turn conversation represents the dominant interaction mode for deployed large language models, yet mounting evidence reveals that model performance degrades severely as conversation history accumulates in the KV-cache. This article investigates three research questions: how rapidly task accuracy declines across conversation turns, what mechanisms drive this degradation at the attention an...

Prompt caching has emerged as one of the most impactful optimizations for reducing both cost and latency in large language model inference, with major providers reporting 50-90% cost savings through prefix reuse. Yet the efficiency of prompt caching varies dramatically across workload types, caching strategies, and eviction policies. This article investigates three research questions: how cache...

The proliferation of open-source large language model families in 2026 — each adopting distinct attention mechanisms and KV-cache configurations — creates a fragmented landscape where memory footprint varies by up to 4.6x across architectures at identical context lengths. This article provides a systematic cross-architecture comparison of KV-cache memory behavior across four dominant model fami...

The KV-cache memory bottleneck in large language model inference has generated three competing families of compression techniques — quantization, token eviction, and structured pruning — each claiming substantial memory savings with minimal accuracy loss. This article benchmarks these approaches head-to-head, drawing on 2026 research that provides standardized comparisons across architectures a...

As large language models advertise context windows spanning millions of tokens, the gap between nominal capacity and effective performance has become a central concern for deployment. This article investigates memory degradation curves — the systematic decay of model accuracy as context length increases — drawing on 2026 research that isolates context length as an independent variable affecting...

As large language models extend their context windows to millions of tokens, the critical question shifts from capacity to capability: can models actually retrieve and reason over information distributed across vast inputs? This article examines the evolution and current state of long-context retrieval benchmarks in 2026, from the foundational Needle-in-a-Haystack (NIAH) test to sophisticated m...

Modern large language models advertise context windows ranging from 128K to 10M tokens, yet empirical benchmarks consistently reveal a substantial gap between advertised capacity and effective utilization. This article presents a systematic analysis of context window utilization across frontier LLMs, examining the divergence between theoretical context length and the operational window within w...

The key-value (KV) cache is the operational memory of transformer-based large language models (LLMs), storing intermediate attention representations that grow linearly with sequence length and quadratically impact computational cost. Yet what exactly do models store in these key and value vectors, and how uniformly is this information distributed across heads and layers? This article presents a...

The key-value (KV) cache is the dominant memory structure enabling efficient autoregressive inference in transformer-based large language models (LLMs). While the self-attention mechanism requires quadratic computation over the full sequence during training, the KV-cache converts inference into a linear-time operation by retaining previously computed key and value projections. This article prov...