deepseek-ai/DeepInfer

What it does

The high-performance inference framework DeepSeek uses in its own production environment; this is its open-source offline edition. "Inference" refers to the process of running a trained model to answer questions, and this framework is optimized specifically for large-scale, high-throughput serving scenarios.

Features

• Includes several optimized compute kernels: FlashMLA, DeepGEMM, DeepEP, TileKernels, and others, all high-performance components open-sourced by DeepSeek itself
• Complete parallelism strategies: DP for the attention layer, balanced CP, and EP plus EPLB (expert load balancing) for the MoE layer
• Supports deterministic sampling and batch invariance — the same input yields the same output regardless of batch size, which helps reproduce experiments
• Asynchronous scheduling, described as a zero-overhead batching scheduler
• Supports structured output

Good fit for

• You want to deploy DeepSeek models yourself to provide inference services
• Teams with requirements for throughput, latency, and reproducibility of results
• Research into efficient inference implementations for MoE models

Advice

• Check the supported model list first (it currently states support for DeepSeek-V4.1-Flash) rather than forcing an unsupported model to run
• Enable deterministic sampling when reproducing experimental results; otherwise the same input may produce different output
• This is for people with GPU clusters and deployment experience, not desktop software you can download and double-click

Cautions

• Only specific models are supported; changing models requires confirming compatibility
• It requires the corresponding GPU hardware and driver environment, so the configuration bar is high
• It targets server-side deployment and will basically not run on a personal computer