Local deployment

What is this?

Local deployment means running an LLM on your own machine or on institution-managed hardware under your direct control, instead of relying on an externally hosted API.

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When should you use it?

• You need stronger control over data locality (e.g., sensitive data that cannot leave your machine)
• You want offline or low-latency experimentation
• You are testing smaller open models for specialized tasks
• You want to validate models before scaling to remote environments
• You have access to local hardware that can support the model size you need
• You want to avoid ongoing costs of remote compute for frequent inference

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When should you NOT use it?

• When you need very large models that exceed local hardware limits
• When cloud or institutional infrastructure is already available and policy-approved
• When you need to collaborate with a team that cannot access the same local environment
• When you want to quickly iterate without the overhead of local setup and maintenance

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How it works (simple explanation)

You install a local inference runtime, download model weights, and run prompts directly on your machine. Performance (latency, throughput) depends heavily on CPU/GPU memory and model size.

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Concrete examples (tools/platforms)

• User-friendly local LLM apps

  • Ollama: easy-to-use local LLM runtime with chat + API interface
  • LM Studio: desktop app for running and chatting with local models
  • Jan.ai： open-source local AI assistant with privacy focus

• Inference engines (core runtime layer)

  • llama.cpp： efficient C++ inference engine for running quantized models locally
  • vLLM： high-performance inference engine for serving LLMs
  • Text Generation Inference: production-ready LLM inference server

• Programming frameworks (research workflows)

  • Hugging Face Transformers： Python library for loading and running LLMs programmatically

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Example workflow (step-by-step)

1. Select a model size your hardware can support.
2. Install a local inference tool and required dependencies.
3. Download model weights from a trusted source.
4. Run a baseline prompt benchmark on sample data.
5. Tune runtime settings (context length, precision, batch size).
6. Validate output quality before wider use.

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Pros and cons

Pros	Cons
Strong data control and local execution	Limited by hardware memory and compute
Useful for offline experimentation	Setup and maintenance burden
Potentially lower long-term per-call cost	Large models may be impractical

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Learning resources

Getting started (quick local setup)

• Ollama docs: beginner-friendly way to run and manage LLMs locally
• llama.cpp: practical guide and examples for running LLMs locally (CPU/GPU, quantization)

Research workflows (core libraries)

• Hugging Face Transformers docs: load models, run inference, build pipelines
• Hugging Face Accelerate: manage CPU/GPU and multi-device setups
• Microsoft Guidance: structured prompting and controlled generation

Serving & scaling (API-style deployment)

• vLLM docs: high-performance inference engine for scalable deployment
• Text Generation Inference (TGI): production-ready LLM serving
• Open WebUI: self-hosted chat interface for local models

Fine-tuning (advanced)

• PEFT (Parameter-Efficient Fine-Tuning): efficient fine-tuning methods like LoRA
• QLoRA: fine-tune large models on limited hardware

Learning & fundamentals

• DeepLearning.AI: Open-Source Models with Hugging Face: structured course on using open models locally
• llm.c: minimal implementation for understanding LLM internals