Memory
5 articles
Articles
23 February 2026
KV Cache Memory Calculation for LLMs: A Technical Guide
Large Language Model (LLM) weights are only half the story. As sequence lengths grow and batch sizes increase, the Key-Value (KV) cache often becomes the primary consumer of GPU VRAM, leading to the dreaded Out-of-Memory (OOM) errors that plague production environments. For ML engineers, understanding the precise memory requirements of the KV cache is not just a theoretical exercise; it is a prerequisite for efficient scaling. This article provides a deep dive into the mechanics of KV caching, the mathematical foundations for memory estimation, and how modern architectures like Llama 3 or Mistral utilize advanced attention mechanisms to mitigate memory bottlenecks.
31 August 2026
Fixing vLLM CUDA Out of Memory: KV Cache Tuning Guide
vLLM CUDA out of memory errors usually stem from startup reservations, not runtime loads. By tuning gpu_memory_utilization and max-model-len, you can right-size the KV cache and stabilize inference without renting larger GPUs.
4 June 2026
Long Context Inference: GPU Requirements & VRAM Guide
Context kills VRAM. Learn the exact math behind KV cache bottlenecks and how to architect your GPU infrastructure for 128K+ token workloads.
31 May 2026
LLM Context Length vs. GPU Memory: Calculating VRAM Requirements
Parameter count only tells half the story. Learn how to calculate the exact GPU memory required for long-context LLM inference and avoid catastrophic Out-of-Memory errors in production.
18 May 2026
GGUF vs GPTQ vs AWQ: The Definitive LLM Quantization Framework
We break down the exact performance, memory, and throughput differences between GGUF, GPTQ, and AWQ for production inference.