Best local coding LLM for my RTX 5060 Ti 16GB? Context window limitations & building full projects from 0 to 100 Hi everyone! I'm looking for advice from experienced local LLM users and developers. I want to use AI not just for generating code snippets, but for building complete applications from scratch using agentic coding workflows. I'm particularly interested in understanding how to work effectively with local models when hardware and context window limitations are significant. 🖥️ My hardware - GPU: NVIDIA RTX 5060 Ti 16GB VRAM - CPU: Intel Core i7-8700 - RAM: 16GB DDR4 - OS: Windows - LLM software: LM Studio + llama.cpp (CUDA) - Goal: Local AI-assisted development, vibe coding, and agentic coding I'm willing to experiment with different quantizations and model sizes, but I want to get the most practical coding performance from my hardware. --- 1 Best coding model for my hardware What is currently the best local LLM for coding and agentic coding that I can realistically run on an RTX 5060 Ti 16GB with 16GB system RAM? I'm considering models in the 14B–27B range, but I'm open to other sizes. My priorities are: - Writing high-quality code - Debugging and fixing errors - Understanding existing codebases - Planning and executing multi-step tasks - Editing multiple files - Tool calling and agentic workflows - Building complete web applications - Following project requirements over long sessions What model would you personally recommend for this hardware, and what quantization would you use? Would a smaller model at Q4/Q5 generally be more effective than a larger 27B model at IQ3/Q3 for practical coding and agentic tasks? --- 2 How important is the context window in real-world coding? I often see models advertised with very large context windows (32K, 64K, 128K, 256K, etc.), but I'm not sure how much context is actually necessary for building applications. I have a few questions: - How important is context length compared to model intelligence and coding quality? - Is 16K or 32K context enough to build a complete web application? - Does a larger context window always improve coding performance? - How much VRAM/RAM does increasing context length consume in llama.cpp? - How should I balance model size, quantization, context length, and KV cache? - Is Q4_K_M with a smaller context better than IQ3 with a larger context for coding? I'm especially interested in practical experience rather than just theoretical benchmarks. --- 3 What should I do when my context window is too small? This is one of my biggest questions. Let's say I'm using a model with a 16K context window, but my project eventually contains thousands of lines of code across dozens of files. How can I continue working effectively without sending the entire project to the model every time? What techniques do experienced developers use? For example: - Repository indexing and code retrieval (RAG) - Embeddings and semantic search - Project summaries and architectural documentation - A structured task list or TODO file - Keeping a persistent project specification - Automatically selecting only relevant files - Breaking large tasks into smaller subtasks - Using Git commits and checkpoints - External memory or agent state - Summarizing previous conversations and continuing in a new context Which of these methods actually work well with local LLMs? Are there any recommended tools, IDE extensions, or agent frameworks that work well with LM Studio or llama.cpp? --- 4 How do you build a complete project from 0 to 100 with a local LLM? I want to understand the actual workflow for building a complete application, not just generating isolated code snippets. For example, imagine I want to build a full-stack web application from scratch. How would you organize the process? Example workflow Define the idea and requirements. Plan the application architecture. Choose the tech stack. Create the project structure. Implement the frontend. Implement the backend and APIs. Set up the database. Add authentication and security. Test and debug. Refactor and improve the code. Deploy the application. Would a local LLM be able to handle this workflow reliably with an agentic coding setup? Or should I divide the project into small, clearly defined tasks and manually supervise each step? How do you maintain consistency across the entire project when the model cannot see all the files and requirements at once? --- 5 Recommended tools and workflow What local coding setup would you recommend for my hardware? I'm currently using LM Studio, but I'm open to other tools if they offer better agentic coding capabilities. I'm interested in: - IDE integrations - Local coding agents - Open-source agent frameworks - MCP / tool calling - File editing and terminal execution - Git integration - Project memory and retrieval - Offline or mostly local workflows I would also appreciate recommendations for a practical workflow that works well on Windows. --- 🎯 My main goal I want to use my PC to build real applications from start to finish with AI assistance, while understanding the limitations of local models and learning how to work around them. I don't expect AI to replace the developer completely. I want to learn how to design the right workflow so that even a model with limited context and hardware can help me build substantial projects. If you have experience with local coding agents, long-context workflows, or building full projects with smaller models, I'd really appreciate your advice. What would you recommend for my hardware, and how would you personally approach building a complete project from 0 to 100? Thanks in advance!   submitted by   /u/Business_Caramel_688 [link]   [comments]