Ollama

Here I am comparing how much VRAM new version of Ollama allocating for the model vs previous Ollama version. The new version is worse.

Ollama has quickly become one of the most popular tools for running LLMs locally. Its simple CLI, and streamlined model management have made it a go-to option for developers who want to work with AI models outside the cloud. But as with many promising platforms, there are already signs of Enshittification:

Locally hosted Ollama allows to run large language models on your own machine, but using it via command-line isn’t user-friendly. Here are several open-source projects provide ChatGPT-style interfaces that connect to a local Ollama.

Since standard Ollama doesn’t have a direct rerank API, you’ll need to implement reranking using Qwen3 Reranker in GO by generating embeddings for query-document pairs and scoring them.

In this test I’m comparing how different LLMs hosted on Ollama translate Hugo page in English to German. Three pages I tested were on different topics, had some nice markdown with some structure: headers, lists, tables, links, etc.

This little Reranking Go code example is calling Ollama to generate embeddings for the query and for eache candidate document, then sorting descending by cosine similarity.

The Qwen3 Embedding and Reranker models are the latest releases in the Qwen family, specifically designed for advanced text embedding, retrieval, and reranking tasks.

How PCIe Lanes Affect LLM Performance? Depending on the task. For training and multi-gpu inferrence - perdormance drop is significant.

In the Ollama models library there are models that able convert HTML content to Markdown, which is useful for content conversion tasks.

Will list here some AI-assisted coding tools and AI Coding Assistants and their nice sides.

I’ve got a theory to test - if utilising ALL cores on Intel CPU would raise the speed of LLMs? This is bugging me that new gemma3 27 bit model (gemma3:27b, 17GB on ollama) is not fitting into 16GB VRAM of my GPU, and partially running on CPU.

When the Ollama server receives two requests at the same time, its behavior depends on its configuration and available system resources.

DeepSeek’s first-generation of reasoning models with comparable performance to OpenAI-o1, including six dense models distilled from DeepSeek-R1 based on Llama and Qwen.

Here is the list and examples of the most useful Ollama commands (Ollama commands cheatsheet) I compiled some time ago. Hopefully it will be useful to you too.

Not long ago was released. Let’s catch up and test how Mistral Small performs comparing to other LLMs.

Reranking is a second step in Retrieval Augmented Generation (RAG) systems, right between Retrieving and Generating.