Chinese artificial intelligence company DeepSeek has released its internal AI inference framework, DeepInfer, as open-source software, aiming to help developers and businesses run large language models more efficiently while reducing computing costs and energy consumption.

Announced on October 20, the release gives developers access to technology DeepSeek uses to serve AI models at scale. The framework is designed to improve processing speed and handle large volumes of requests across computing infrastructure.

What Is DeepInfer and Why Does It Matter?

DeepInfer is an inference framework that helps AI systems generate responses after a model has been trained. It manages computing resources and coordinates workloads so models can process user requests efficiently.

As AI adoption grows, inference has become a major expense for companies operating large language models. Improving efficiency can help reduce the computing power required to generate responses, potentially lowering operating costs for AI providers and their customers.

DeepSeek says its framework combines high-performance computing kernels with an optimized inference engine. It supports distributed inference, speculative decoding and structured outputs, alongside other features designed for large-scale AI deployments.

DeepInfer Supports Nvidia and Huawei Platforms

One notable feature is support for hardware platforms from both Nvidia and Huawei. This allows developers to work with DeepInfer across different computing environments rather than relying on a single hardware ecosystem.

The release comes as competition intensifies over the infrastructure needed to power advanced AI models. Access to efficient software tools is increasingly important for companies seeking to control computing expenses while maintaining performance.

However, practical results will depend on the hardware configuration, model workload and deployment environment. Independent testing will be needed to establish how much DeepInfer improves efficiency across different applications.

DeepSeek Reports 155 Trillion Tokens Processed Daily

DeepSeek said its DeepInfer-based infrastructure already supports large-scale inference services for its V4.1 Flash model. According to the company, a single computing cluster currently serves 155 trillion tokens per day.

Tokens are the units of text that AI models process when interpreting prompts and generating responses. The figure highlights the scale of DeepSeek’s reported operations, although the company has not independently verified the performance claim in the announcement.

The framework’s ability to coordinate workloads across large computing clusters is central to its purpose. Better scheduling and processing efficiency can help AI providers serve more requests without proportionally increasing their infrastructure requirements.

DeepSeek Also Releases the DeepSeek-Recipe Component

Alongside DeepInfer, the company announced the open-source release of deepseek-recipe, a component designed to handle token encoding and decoding while connecting mainstream large language model API protocols with model token sequences.

DeepSeek says the component’s processing behavior aligns with its official API. This could make it easier for third-party developers to deploy its technology while maintaining compatibility with existing applications.

The company also said inference code for future models and subsequent performance improvements would continue to be released through DeepInfer, potentially expanding the tools available to developers building AI-powered products.

Open-Source AI Competition Expands

The release adds another infrastructure tool to the growing open-source AI ecosystem. Developers and businesses can use open-source software to inspect, adapt and deploy technology, subject to the applicable license terms.

For AI companies, more efficient inference can influence both service pricing and the economics of running models at scale. Lower infrastructure costs may also make advanced AI applications more accessible to smaller developers and organizations.

DeepSeek said it expects broader access to models, operator libraries and inference frameworks to encourage further development and reduce wasted energy across AI infrastructure.