For developers, system architects, and technical power users, running local AI is not just about executing basic text queries. It is about maximizing efficiency, precision, and performance within stri
When your prompt, system instructions, and chat history exceed the maximum token capacity allocated to the model, performance rapidly degrades. You might face runtime crashes, silent text truncation,
[IMAGE: Code snippet demonstrating how to increase the Ollama context window size in a Modelfile]
To fix memory loss in local AI systems, you first need to understand the fundamental architecture of neural language models.
Before jumping into settings, it helps to clear up a common misconception about local model runners.
When configuring local large language models, performance depends on three core parameters: total parameter size, quantization level, and context window allocation. Tuning these settings determines wh
As context windows expand across modern Large Language Models (LLMs)—with standard models accepting 200,000 tokens and specialized models supporting over 1,000,000 tokens—ai architects face a major de
Anthropic’s Claude 3.5 Sonnet and Claude 3 Opus models offer standard 200,000-token context windows (and specialized enterprise windows reaching up to 1,000,000 tokens). This expanded capacity has tra
Context window limitations are the silent killer of AI agent projects. Your agent works perfectly in short conversations, then mysteriously starts forgetting previous interactions, losing track of mul