## Local LLM Development: A Challenge for Mid-Range GPUs? A user on the LocalLLaMA forum has raised an interesting question regarding the local development of large language models (LLMs), i.e., directly on their machine instead of using cloud services. The user, equipped with an Nvidia RTX 5070 Ti GPU with 16GB of VRAM, encountered difficulties using Kilo code with the Qwen 2.5 coder 7B model via Ollama. The main problem lies in the limited context size, which fills up quickly even with a single project file. The question posed to the community is therefore very practical: how do other developers with 16GB GPUs manage local LLM development effectively? This opens a debate on optimization strategies, the choice of the most suitable models, and possible alternative solutions to overcome hardware limitations. ## General Considerations on Local LLM Development Local LLM development offers advantages in terms of privacy, control, and costs, avoiding dependence on external services. However, it requires an adequate hardware configuration, in particular a GPU with a sufficient amount of VRAM. Efficient context management is crucial for achieving meaningful results, and often requires a trade-off between model size, context length, and available resources.