从 1600 美元单卡到 450 万美元年费：部署大模型到底需要多少钱？

Core Insights - The article discusses the significant cost disparities between local deployment of AI models and subscription-based commercial APIs, highlighting the need for a clear cost analysis framework for businesses considering generative AI integration [1][2][5]. Cost Analysis Framework - A systematic cost analysis framework has been developed to compare total ownership costs (TCO) of local deployment (hardware, electricity) versus commercial APIs (subscription fees) [2][5]. - The framework includes an online cost estimation tool tailored for different business sizes, allowing companies to analyze their specific workloads [2][3]. Local Deployment Costs - Local deployment costs vary by model size: small models (e.g., EXAONE 4.0 32B) can be deployed with a single RTX 5090 GPU (approximately $2,000) and monthly electricity costs of $13.2; medium models (e.g., Llama-3.3-70B) require one A100 GPU ($15,000) with monthly costs of $7.92; large models (e.g., Qwen3-235B) need four A100 GPUs ($60,000) with monthly costs of $31.68 [2][3][21]. - Hardware costs account for over 90% of the initial investment in local deployment [2]. Commercial API Costs - Commercial APIs charge based on token usage, with significant price differences: high-end services like Claude-4 Opus charge $15 for 1 million input tokens and $75 for output, while cost-effective options like GPT-5 charge $1.25 for input and $10 for output [2][20]. - For a monthly processing of 50 million tokens, the annual cost for high-end services can exceed $4.5 million, while cost-effective options may only cost $375,000 [2]. Break-even Analysis - The break-even period varies significantly: small models can achieve break-even in as little as 0.3 months compared to high-end commercial APIs, while medium models take between 2.3 to 34 months, and large models can take from 3.5 to 108 months [2][3]. - A monthly processing threshold of 50 million tokens is critical for the economic viability of large model local deployments [2]. Market Context - The rapid development of LLMs has led to increased interest in local deployment due to concerns over data privacy, vendor lock-in, and long-term operational costs associated with commercial APIs [5][7]. - The article emphasizes the growing feasibility of local deployment for small and medium enterprises, driven by advancements in open-source models and hardware [12][50]. Strategic Decision Framework - The research categorizes deployment scenarios into three types: quick return on investment (0-6 months), long-term investment (6-24 months), and economically unfeasible (over 24 months), aiding organizations in making informed decisions [49][50]. - The findings suggest that local deployment is not as straightforward as previously thought, with various factors influencing the economic viability of different deployment strategies [48][52].