Browsing: Cache

Most end-to-end OCR models slow down as output grows. Each generated token adds to the KV cache. Memory rises and generation…

Long-context large language models (LLMs) face a memory bottleneck that has nothing to do with model weights. During decoding, transformers cache the key and value (KV)…

Long-context inference makes the KV cache one of the main costs of serving LLMs. During autoregressive decoding, the cache grows with context length, batch size, and…

As large language models scale to longer context windows and serve more concurrent users, the key-value (KV) cache has emerged as a primary memory bottleneck in…

import numpy as np import matplotlib.pyplot as plt fig, axes = plt.subplots(1, 2, figsize=(14, 4.5)) tk, mk = zip(*mem_kvc); tb, mb = zip(*mem_base) axes[0].plot(tk, mk, label=”with…

any time with Transformers, you already know attention is the brain of the whole operation. It is what lets the model figure out which tokens are…

Long-chain reasoning is one of the most compute-intensive tasks in modern large language models. When a model like DeepSeek-R1 or Qwen3 works through a complex math…

In this tutorial, we take a detailed, practical approach to exploring NVIDIA’s KVPress and understanding how it can make long-context language model inference more efficient. We…

The scaling of Large Language Models (LLMs) is increasingly constrained by memory communication overhead between High-Bandwidth Memory (HBM) and SRAM. Specifically, the Key-Value (KV) cache size…

, we talked in detail about what Prompt Caching is in LLMs and how it can save you a lot of money and time when running…