8 posts
Where does an open source LLM stand in enterprise use? License terms, closed-model comparison, operational load, and in which scenario it makes sense.
Open source LLM comparison: the strengths, licenses, sizes, Turkish performance of Llama, Qwen, Mistral and DeepSeek, plus an enterprise model selection framework.
What is Ollama? Ollama is an open-source tool that lets you download and run large language models on your own computer or server with a single command. This guide: a clear definition, how Ollama works, running LLMs locally, GGUF and the model library, hardware requirements, Ollama vs cloud API, and FAQs.
What is Llama? Llama is a family of large language models (LLMs) developed by Meta whose model weights are released openly to everyone. This guide: a clear definition, how Llama works, what an open-weight model means, versions and variants, running it as a local LLM, the Llama license, enterprise and Türkiye use cases, KVKK, comparison with closed models, and FAQs.
What is an open-source LLM? An open-source LLM is a large language model whose weights are published openly so you can run it on your own infrastructure. This guide: a clear definition, open weights vs open source, local deployment, licensing, Llama and other models, KVKK/GDPR, comparison with closed models, and FAQs.
What is an LLM? How do Large Language Models (LLMs) work, what does Transformer architecture solve, what are tokens, embeddings, and context windows, and how do GPT-5, Claude Opus 4.7, Gemini 3, and Llama 4 compare? A comprehensive 2026 reference covering Turkish LLM performance, training stages, hallucination control, and cost modeling.
Detailed comparison of the three most powerful 2026 open-weight LLM families — DeepSeek (V3 + R1), Qwen (2.5 + 3), and Meta Llama (4). Architecture (MoE vs dense), benchmarks (MMLU, HumanEval, GSM8K), Turkish performance, license (MIT vs Apache vs Llama Community), cost (self-hosted vs API), hardware (VRAM, GPU), fine-tune friendliness, ecosystem (Hugging Face, vLLM, Ollama), KVKK / data sovereignty advantages. Use cases for Turkish enterprises.
The most current, detailed 2026 Turkish guide to adapting an LLM to your domain. Covers when fine-tuning is necessary, the math behind LoRA, 4-bit training with QLoRA, why DPO beats PPO, modern alternatives (ORPO/KTO/IPO), Turkish dataset sources, GPU/cloud cost modeling, production pipelines, 3 anonymized Turkish enterprise case studies, and KVKK-compliant training. For developers, MLOps engineers, and AI architects.