LLM 推理成本优化：运行任何模型只需几分钱

description: ‘LLM inference cost optimization guide. Compare Ollama, vLLM, llama.cpp quantization. Reduce API costs by 90%+. 3 benchmarks, 6 deployment methods.’ tags: [‘guide’, ‘open-source’, ‘reference’, ’tutorial’] date: 2026-06-16 slug: ’llm-inference-cost-optimization-guide-2026’ category: dev-utils github_repo: ‘https://github.com/ollama/ollama' license: MIT lang: zh faqs: #

The first time I saw an OpenAI API bill for $47.32, I stared at my screen for a full minute. Not because it was a lot of money. But because I had been running experiments for 4 hours on a $20/month GPU that I found on a discount deal.

That’s when I realized: we’re all paying too much for LLM inference.

Every developer who’s used ChatGPT API or Claude API has felt this pain. The per-token pricing looks reasonable — until you actually use it. Then the numbers add up fast.

This is not a tutorial. This is what I learned after testing every major inference engine for 3 months, measuring actual costs, and building a comparison that doesn’t rely on benchmarks from the companies selling you the solution.

The Real Cost of LLM Inference (Not What Companies Tell You) #

Let’s be honest about pricing. Here’s what you actually pay per million tokens for the most common models:

| Model | Input ($/M tokens) | Output ($/M tokens) | Cost per 1K tokens | |