📄 中文摘要
Ollama在2026年第一季度达到了5200万次月下载量，相较于2023年第一季度的10万次增长了520倍。HuggingFace托管的135,000个优化用于本地推理的GGUF格式模型，三年前仅为200个。驱动这一基础设施的llama.cpp项目在GitHub上的星标数超过73,000。这些数据反映了行业的转变，本地推理在消费硬件上以零边际成本提供70-85%的前沿模型质量。文章提供了支持这一论断的基准数据、硬件成本模型和生产模式。
2026年的本地AI：Ollama基准测试、零推理成本与每个令牌定价的终结
📄 English Summary
Local AI in 2026: Ollama Benchmarks, $0 Inference, and the End of Per-Token Pricing
Ollama achieved 52 million monthly downloads in Q1 2026, marking a 520-fold increase from 100,000 in Q1 2023. HuggingFace hosts 135,000 GGUF-formatted models optimized for local inference, up from just 200 three years ago. The llama.cpp project, which underpins much of this infrastructure, has surpassed 73,000 stars on GitHub. These figures indicate a significant industry shift, as local inference on consumer hardware delivers 70-85% of frontier model quality at zero marginal cost per request. The article presents benchmark data, hardware cost models, and production patterns that support this claim.