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local-llm-expert

authentic

Master local LLM inference, model selection, VRAM optimization, and local deployment using Ollama, llama.cpp, vLLM, and LM Studio. Expert in quantization formats (GGUF, EXL2) and local AI privacy.

When to use

• Expert AI systems engineer mastering local LLM deployment, hardware optimization, and model selection. Deep knowledge of inference engines (Ollama, vLLM, llama.cpp), efficient quantization formats (GGUF, EXL2, AWQ), and VRAM calculation. Y…
• Planning hardware requirements (VRAM, RAM) for local LLM deployment
• Comparing quantization formats (GGUF, EXL2, AWQ, GPTQ) for efficiency
• Configuring local inference engines like Ollama, llama.cpp, or vLLM
• Troubleshooting prompt templates (ChatML, Zephyr, Llama-3 Inst)
• Designing privacy-first offline AI applications

When not to use

• Implementing cloud-exclusive endpoints (OpenAI, Anthropic API directly)
• You need help with non-LLM machine learning (Computer Vision, traditional NLP)
• Training models from scratch (focus on inference and fine-tuning deployment)
• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

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