SiliconFlow Outlines Its Approach to Scaling LLM Deployment at State-Owned Enterprises
从“算力”到“Token 生产力”，硅基流动助力央国企大模型规模化落地
- Who
- Tang Anbo
- What
- presented inference infrastructure and ROI practices for scaling LLMs in state-owned enterprises
- Quote
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What happened
At a 2026 summit for CIOs and executives at central and state-owned enterprises, SiliconFlow solutions director Tang Anbo described how the company helps organizations turn computing capacity into token services through model operations, inference optimization, and resource scheduling. In one customer case, about 50 machines were reorganized to serve roughly 10 models through Serverless APIs; the cost per million tokens for one Qwen model fell from over 100 yuan to under 10 yuan.
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See the full storySiliconFlow describes enterprise LLM deployment approach and reports lower inference costs in one customer caseThe story so far
SiliconFlow reorganized about 50 machines at one customer to serve roughly 10 models through Serverless APIs, cutting the cost per million tokens for one Qwen model from over 100 yuan to under 10 yuan, according to solutions director Tang Anbo. Speaking at a summit for CIOs and executives at central and state-owned enterprises, Tang described how SiliconFlow uses model operations, inference optimization, and resource scheduling to turn computing capacity into token services. The customer example illustrated that approach; the reported cost reduction applied to one Qwen model.
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