Why Local LLM Operations Now?
Cloud LLMs are convenient and high-performance, but cost visibility, handling of confidential data, latency (response delay), and API limits can be obstacles. What is gaining attention is self-hosting large language models (LLMs) locally or in your own environment.
The魅力 of local operations can be summarized in three main points.
- Data stays in-house: well-suited for RAG (retrieval-augmented generation) that handles internal documents and customer information
- Cost control: as usage grows, it tends to be more favorable than pay-as-you-go
- Customization: high flexibility for model swapping, quantization, inference settings, and monitoring
This article focuses on Ollama for rapid exploration and vLLM, a production-grade high-throughput inference server, and summarizes practical points for operations beyond merely getting things running.
Overview: Choosing Between Ollama and vLLM
Clarifying roles up front reduces confusion.
Ollama: The fastest route for local development and prototyping
Ollama is a tool that bundles model acquisition, startup, and execution, making it ideal for trying things locally first. It is easy to install on Mac/Windows/Linux, and model management is straightforward. It’s convenient for running PoCs within a team.
vLLM: A production-grade inference backbone for high load
vLLM optimizes inference (notably PagedAttention), making it easier to achieve throughput on a single GPU. It offers configurations that can present an OpenAI-compatible API, which eases migration for apps. Its strengths show in long-running operations, high concurrency, and team-based usage.
Recommended approach
Start with Ollama for model selection → internal evaluation → once requirements are solid, move to production with vLLM. That flows smoothly.
Preparation: Realistic Hardware and Model Selection Realities
GPU/VRAM guidelines
Local LLMs require resources that vary with model size and quantization (reducing precision to lighten). Rough guidelines are as follows.
- 7B–8B: quantization makes VRAM ~6–10GB practical (for development and chat use)
- 13B–14B: VRAM 12–24GB is comfortable (balance of quality and speed)
- 30B+: VRAM ~48GB or multi-GPU setups (serious production backbone)
CPU inference is possible, but perceived speed depends on the use case. For day-to-day internal tools, GPU operation tends to be less stressful.
Allocate models by use-case
Chasing a universal model can be tempting, but operation is more stable when you tailor by use-case.
- Chat/Summary: a model that excels at following generic instructions
- Code assistance: models strong with code
- Internal Q&A: RAG design (search quality) often determines success
Additionally, if you value Japanese language quality, compare derived models skilled in Japanese and benchmark with evaluation sets (e.g., Japanese QA datasets) to avoid later trouble.
