DeepInfer

DeepSeek's open-source, high-performance inference framework that powers its own production services — offline inference for DeepSeek-V4.1-Flash on NVIDIA SM100 GPUs and Huawei Ascend 950 NPUs

Open Source Coding & Development
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What is DeepInfer?

DeepInfer is the high-performance inference framework that powers DeepSeek's large-scale production services, now open-sourced under Apache 2.0: it provides offline inference with the core features needed for serving at scale.

The first release supports DeepSeek-V4.1-Flash on NVIDIA SM100 GPUs and Huawei Ascend 950 NPUs, with published prefill and decode benchmarks. It is built from source on Linux (GCC 13+, Python 3.12, CUDA 13 or CANN 9.2), and a KV cache system, disaggregation and load balancing are explicitly left for you to add.

Key features

  • Optimized kernels — FlashMLA, DeepGEMM, DeepEP, TileKernels and custom TileLang kernels
  • Parallelism: Attention DP / balanced CP, MoE EP + EPLB
  • DSpark and confidence-scheduled verification to accelerate decoding
  • SWA bounded replay to cut decoder prefill work
  • Asynchronous zero-overhead batch scheduling
  • Batch-invariant inference and deterministic sampling
  • Structured outputs constrained to a regex, JSON schema or EBNF grammar

Pros

  • Apache 2.0 open source
  • Powers DeepSeek's own production services
  • Published prefill and decode benchmarks
  • Built on the vLLM lineage, with SGLang and TensorRT-LLM inspiration

Cons

  • First release supports one model family (DeepSeek-V4.1-Flash) and offline inference only
  • No KV cache system, disaggregation or load balancing in this release — you build those yourself

DeepInfer at a glance

PricingOpen Source
CategoryCoding & Development
Platformslinux
CompanyDeepSeek
Best forTeams serving DeepSeek models at scale on their own hardware
Official websitegithub.com
inferenceDeepSeekopen sourceLLM servingGPU

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DeepInfer FAQs

What is DeepInfer?

DeepInfer is the high-performance inference framework that powers DeepSeek's large-scale production services, now open-sourced under Apache 2.0: it provides offline inference with the core features needed for serving at scale.

What are the main features of DeepInfer?

DeepInfer offers Optimized kernels — FlashMLA, DeepGEMM, DeepEP, TileKernels and custom TileLang kernels, Parallelism: Attention DP / balanced CP, MoE EP + EPLB, DSpark and confidence-scheduled verification to accelerate decoding, SWA bounded replay to cut decoder prefill work, Asynchronous zero-overhead batch scheduling, Batch-invariant inference and deterministic sampling and Structured outputs constrained to a regex, JSON schema or EBNF grammar.

How much does DeepInfer cost?

DeepInfer is open source, so the software itself is free to download, use and self-host. Your only costs are the infrastructure you run it on and any paid services you connect to it.

What are the pros and cons of DeepInfer?

Strengths of DeepInfer include Apache 2.0 open source; Powers DeepSeek's own production services; Published prefill and decode benchmarks. On the downside, First release supports one model family (DeepSeek-V4.1-Flash) and offline inference only; No KV cache system, disaggregation or load balancing in this release — you build those yourself.