DeepSeek has open-sourced its inference framework, DeepInfer—the production-grade system responsible for orchestrating compute clusters and enabling models to efficiently handle workloads. The model (V4.1-Flash) was already open-sourced; this release unveils the “engine” that runs it. This is the first time any top-tier global large model company has done so—previously, such technologies were closely guarded trade secrets. This indicates: 1. Competition has shifted from “model performance” to “cost efficiency.” Model capability gaps are narrowing; the winner will be the one who can deliver the same service using fewer GPUs and lower electricity costs. DeepSeek has publicly disclosed how much revenue each GPU generates per hour—essentially showcasing: “My unit cost is lower than yours.” 2. Open-sourcing has become a strategic weapon. Timing-wise, on October 19, the U.S. removed DeepSeek from federal procurement channels; on October 20, DeepSeek open-sourced its core engine. Open-sourcing = binding a global developer ecosystem = rendering U.S. bans ineffective. The pressure on closed-source competitors—especially OpenAI—is real: cloud providers deploying with DeepInfer may achieve even lower costs than using the official native solution. 3. The value of the “middle layer” has been openly priced. Previously, everyone focused only on model benchmarks; now it’s clear: the same model can differ by several times in cost depending on the quality of the inference framework. This demystifies technology for domestic inference infrastructure players (vLLM, SGLang ecosystem, Ascend compatibility). 4. Practical benefits for ordinary developers: lower deployment costs for cloud providers → potential for further API price reductions; lower barriers to local deployment → cheaper private deployments. Compatibility with both NVIDIA and Huawei Ascend platforms is especially critical for domestic teams pursuing private deployments. 5. A sobering note: The most critical components—KV cache management and load balancing—are not fully open-sourced, as they are too tightly coupled with internal infrastructure; only design documents have been published. Thus, it’s “semi-transparent”—you learn the concepts, but don’t get full capabilities. Additionally, the open-source version currently supports only offline inference. By publicly releasing a production-grade inference system that competitors treat as confidential, DeepSeek is countering compute restrictions with an open ecosystem. In the short term, this benefits everyone who can afford AI; in the long term, it puts pressure on closed-source vendors. #AI