Running LLMs Locally: A Practical Guide: Field Notes and Practical Analysis

Local LLMs are now good enough for a lot of everyday work, especially when privacy, offline access, or cost control matters more than peak reasoning. This guide keeps it practical: when to go local, which runtime to pick, what hardware you actually need, and a short set of resources to keep you current.

Quick decision: local vs cloud

If you need…	Choose	Why
Sensitive data stays on your machine	Local	No external API calls
Offline access	Local	No internet dependency
Best-available reasoning and long context	Cloud	Frontier models still win here
Team-wide API at scale	Depends	vLLM or cloud, based on hardware
Fast iteration without per-token costs	Local	Predictable cost after hardware

Start here: pick a runtime

Choose one runtime and get it working before you optimize. The biggest mistake is mixing tools too early.

Ollama (fastest on-ramp)

Simple CLI + API server. Great default if you want quick installs and a local OpenAI-compatible endpoint.

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curl -fsSL https://ollama.com/install.sh | sh
ollama run llama3

Docs and install steps live here: Ollama documentation.

LM Studio (best desktop UI)

GUI for browsing, downloading, and chatting with models. It can also expose a local API server. Ideal if you want a visual interface or you’re switching models frequently.

Get started here: LM Studio docs.

llama.cpp (max control)

The core engine for running GGUF models on CPU or GPU. If you want fine-grained performance tuning or minimal dependencies, this is the foundation.

Project home: llama.cpp on GitHub.

vLLM (multi-user serving)

High-throughput inference server with OpenAI-compatible endpoints. Best for teams, shared GPUs, or production-like serving.

Docs: vLLM documentation.

Hardware planning (rough rules)

These are ballpark numbers for 4-bit quantized models. Context length, quantization, and backend matter a lot, so treat these as starting points.

Model size	Typical fit	What to expect
3B to 8B	6 to 10 GB VRAM or 16+ GB RAM	Works on laptops, good for simple tasks
10B to 20B	12 to 24 GB VRAM or 32+ GB RAM	Solid general use if prompts are short
30B to 70B	48+ GB VRAM or multi-GPU	Great quality, heavy hardware

Notes that matter:

Bigger context windows consume more memory. If you raise context from 4k to 32k, expect a large VRAM jump.
Apple Silicon uses unified memory, so “VRAM” is your system memory budget.
CPU-only is viable for small models, but latency increases quickly as size grows.

Model selection without guesswork

Don’t chase rankings first. Start with your task and measure.

Pick a task family: general chat, coding, reasoning, or vision.
Choose the smallest model that fits your hardware.
Run 5 to 10 real prompts and measure latency and quality.
Move up in size only if the answers are consistently weak.

Good starting points to explore on any runtime:

General chat: Llama, Qwen, Mistral families.
Coding: Qwen Coder, DeepSeek Coder, StarCoder-style models.
Reasoning: distilled reasoning models can be strong at 7B to 32B.
Vision: look for “vision” or “VL” variants and test with your own images.

If you want a curated model catalog, use the Ollama model library or browse the Hugging Face Model Hub.

Serving patterns

Pick the lightest setup that matches your needs.

Single-user chat: Ollama or LM Studio with local chat UI.
Local API for apps: Ollama, LM Studio, or llama.cpp server.
Team or production serving: vLLM with GPU scheduling and batching.

Cost and safety tradeoffs

Local doesn’t automatically mean safe or cheap.

Costs: You trade per-token fees for hardware, power, and maintenance.
Privacy: Local inference helps, but only if your toolchain doesn’t send telemetry or phone home.
Security: If you connect tools, files, or external data sources, treat it like a real system. Consider isolation (VM or dedicated machine), and don’t reuse personal API keys.
Licensing: Always check the model card and license before shipping anything serious.

Hugging Face explains model cards and licenses here: Model cards and Licenses.

Quick start checklist

Define your primary use case and acceptable latency.
Pick a runtime (Ollama or LM Studio for most people).
Choose a small model first and verify it runs smoothly.
Measure tokens/sec on your actual prompts.
Increase size or context only when you hit real limits.
Review model license and intended use.

Resources

/posts/always-on-vpn/ - Isolation levels for agent-style tools
/posts/openclaw-security-reality-2026/ - Security tradeoffs of self-hosted agents
/posts/honest-limitations-ai-tools-2026/ - Where local models still fall short