DeepSeek Open-Sources Internal Inference Framework DeepInfer to Cut AI Costs
DeepSeek has fully open-sourced its internal inference framework, DeepInfer, alongside the deepseek-recipe token codec component to help reduce large-scale LLM serving costs. The framework powers high-throughput, low-latency deployments such as DeepSeek V4.1 Flash and supports both NVIDIA and Huawei Ascend platforms. By sharing its production-grade inference engine, DeepSeek aims to optimize throughput and latency for developers and enterprises, significantly reducing computational and energy costs. This move promotes broader AI accessibility and democratizes large-scale model deployment across the industry. DeepInfer integrates high-performance operators and supports distributed inference, speculative decoding, SWA Bounded Replay, and structured outputs. Additionally, it ensures API compatibility with official deployments through the open-sourced deepseek-recipe component.
## BACKGROUND
Large language model inference refers to the phase where a trained model processes inputs and generates outputs for users, consuming massive computational power and energy clusters. Frameworks sitting between hardware and models dictate how efficiently these compute resources are managed, impacting overall service costs.