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DeepSeek Opens Source AI Inference Engine
21 Oct
Summary
- DeepSeek open-sourced its DeepInfer inference framework.
- The framework supports NVIDIA and Huawei hardware.
- DeepInfer aims to lower AI compute costs and energy use.
DeepSeek has publicly released its DeepInfer framework, the core technology behind its large-scale AI inference services. This open-source initiative, announced recently, aims to significantly reduce compute costs and energy usage in the AI sector.
The DeepInfer framework is engineered to support the DeepSeek-V4.1-Flash model and is compatible with NVIDIA GPUs and Huawei Ascend NPUs. The company plans to extend support to future hardware systems.
Key features of DeepInfer include optimized kernels, advanced parallelism techniques for attention and mixture-of-experts layers, and an efficient decoding method. It also offers asynchronous scheduling and batch-invariant inference, though some features are specific to NVIDIA hardware.
This release provides offline inference capabilities but requires users to implement their own KV cache systems, disaggregation of prefill and decode, and load balancing. The framework operates on Linux with specific compiler and CUDA/CANN requirements.
DeepSeek built DeepInfer primarily on vLLM, drawing inspiration from SGLang and TensorRT-LLM. The framework's release under the Apache License 2.0 highlights DeepSeek's commitment to transparency and community support in advancing AI accessibility. This move is significant as AI companies often keep such production techniques proprietary.
