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Comparing LLMs performance on Ollama on 16GB VRAM GPU

glukhov.org · published 31 January 2026

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https://www.glukhov.org/llm-performance/

glukhov.org

16 GB VRAM LLM benchmarks with llama.cpp (speed and context) Read at the source ↗

Agentic LLM Inference Parameters Reference for Qwen 3.6 and Gemma 4 Read at the source ↗

Test: How Ollama is using Intel CPU Performance and Efficient Cores Read at the source ↗

Ollama CLI Cheatsheet: ls, serve, run, ps + commands (2026 update) Read at the source ↗

Move Ollama Models to different location Read at the source ↗

How Ollama Handles Parallel Requests Read at the source ↗

KV Cache on 16 GB GPUs: Making Long Context Actually Fit Read at the source ↗

Constraining LLMs with Structured Output: Ollama, Qwen3 & Python or Go Read at the source ↗

Chat UIs for Local Ollama Instances Read at the source ↗

Integrating Ollama with Python: REST API and Python Client Examples Read at the source ↗

Go clients for Ollama: SDK comparison and Qwen3/GPT-OSS examples Read at the source ↗

Ollama Enshittification - the Early Signs Read at the source ↗

Ollama vs vLLM vs LM Studio: Best Way to Run LLMs Locally in 2026? Read at the source ↗

Docker Model Runner vs Ollama (2026): Which Is Better for Local LLMs? Read at the source ↗

Self-hosting Perplexica - with Ollama Read at the source ↗

The Efficient Frontier of Open Models: Finding the Sweet Spot in 2026 Read at the source ↗

https://ollama.com/docs

ollama.com

https://github.com/ollama/ollama

github.com

https://huggingface.co/Qwen

huggingface.co

https://mistral.ai/models/

mistral.ai

https://developer.nvidia.com/cuda-toolkit

developer.nvidia.com