Locally hosted LLM: hardware, models, and deployment

Running a locally hosted LLM felt like a novelty project two years ago. In 2025, it's a legitimate infrastructure choice. Meta's Llama 3.1 70B runs at 40 tokens/second on a single $2,000 GPU. Mistral's models match GPT-3.5 quality at a fraction of the latency. The hardware floor keeps dropping while model efficiency keeps climbing.

The question isn't "can you run an LLM locally?" — it's "should you, given your specific constraints?" The answer depends on three factors: data sensitivity, latency requirements, and cost at your query volume.

According to a 2025 Andreessen Horowitz survey of enterprise AI adopters, 34% now run at least one LLM on-premise or on dedicated hardware — up from 11% in 2023. The privacy-first crowd isn't waiting for cloud providers to solve their compliance concerns.

Unlike generic AI automation posts, this guide shows real CodeWords workflows — not just theory. You'll understand when local hosting makes sense, what hardware you need, and how to integrate local models into production automation.

Think of a locally hosted LLM as a private chef versus a restaurant. Higher upfront cost, full control over ingredients, and no one else sees what you're cooking.

APP: CodeWords — build automation workflows that connect to both cloud LLMs (OpenAI, Anthropic, Gemini) and local models through flexible integrations.

TL;DR - Local LLMs make sense for data-sensitive workloads, high-volume inference (1000+ queries/day), and latency-critical applications - Minimum viable hardware: 16GB VRAM GPU for 7B models, 48GB+ VRAM for 70B models, with quantization trading quality for memory - Deployment tools (Ollama, vLLM, llama.cpp) have matured to production-grade — the setup cost is hours, not weeks

When does a locally hosted LLM beat cloud APIs?

Four scenarios where local wins clearly:

1. Data never leaves your network Healthcare records, legal documents, financial data, proprietary code. If your compliance team won't approve sending data to OpenAI's servers (even with their data processing agreements), local hosting is the only option. HIPAA, SOC 2, and GDPR requirements often mandate this.

2. Cost crossover at volume Cloud API pricing (GPT-4o at ~$5/million input tokens, Claude at ~$3/million) becomes expensive at scale. At 10,000+ queries per day with substantial context windows, local hosting pays for itself within 3–6 months. A $5,000 GPU running 24/7 costs roughly $0.50/day in electricity — serving unlimited queries.

3. Latency-critical applications Cloud APIs add 200–500ms of network latency per request before the model even starts generating. Local inference eliminates this entirely. For real-time applications (autocomplete, live moderation, interactive agents), that latency difference matters.

4. Custom fine-tuned models If you've fine-tuned a model on proprietary data, running it locally gives you full control over the inference stack — no dependency on a provider's fine-tuning API limitations or pricing.