DeepSeek DeepInfer

DeepSeek DeepInfer

DeepInfer is DeepSeek's open-source inference framework for DeepSeek-V4.1-Flash. Find hardware requirements, Linux setup instructions and an explanation of what you still need to build an online API service.

Updated · DeepSeek V4 Hub

Check deployment requirements Official GitHub repository

Can you deploy DeepInfer on your hardware?

Check the requirements below before downloading the model or renting hardware. The current release supports offline inference with DeepSeek-V4.1-Flash.

Model
DeepSeek-V4.1-Flash; default checkpoint: DeepSeek-V4.1-Flash-1017
Accelerators
NVIDIA SM100 GPUs or Huawei Ascend 950 NPUs
Environment
Linux, Python 3.12 and GCC 13.0 or later
Platform runtime
CUDA 13 for NVIDIA; CANN 9.2 for Ascend
Release scope
Offline inference; an online service needs additional infrastructure

The README does not list support for consumer GPUs, Windows or Macs. You also need enough memory for the model and the requests you plan to run.

DeepInfer deployment guides

Start with the hardware guide if you are choosing a machine. If you already have a supported Linux system, go to the build instructions. We checked both guides against the official documentation; we have not run them on the listed accelerators.

  1. Step 1 · Before allocating hardware

    DeepInfer hardware requirements and deployment limits

    Check DeepInfer's supported GPUs, NPUs and Linux requirements. Work out memory needs and see which parts of an online service you must build yourself.

  2. Step 2 · On a supported Linux machine

    How to build DeepInfer and run offline inference

    Build DeepInfer on Linux and run the Flash-1017 offline example. Includes environment setup, checkpoint paths and checks for your first inference run.

DeepInfer news and technical analysis

Published

What DeepInfer Means for the LLM Inference Ecosystem (Oct 2026)

DeepSeek open-sourced DeepInfer on October 20, 2026. The release provides offline inference for DeepSeek-V4.1-Flash on NVIDIA SM100 and Huawei Ascend 950. Its value is a concrete reference for model and engine co-design: architectural KV reuse, bounded replay, and shared model logic across two hardware paths. A complete online service still needs cache storage, disaggregated deployment, and load balancing. The chip-market implications below are editorial analysis, not a cross-platform performance test.

Read the full technical analysis

Published

DeepSeek open-sources DeepInfer inference framework and DeepSeek-recipe codecs, benefiting third-party deployments

DeepSeek released DeepInfer on October 20 alongside the deepseek-recipe codec libraries. DeepInfer exposes the inference engine behind V4.1 Flash, including distributed execution, DSpark speculative decoding, SWA bounded replay and asynchronous scheduling. Recipe handles conversion between API protocols and model tokens. The initial repository provides offline inference; a complete online service still needs distributed KV storage, deployment across separate serving stages and load balancing across replicas. The release shows how model architecture, cache lifetime and hardware scheduling can be optimized together.

Read the full technical analysis

How should you read the performance claims?

The official prefill and decode charts use different numbers of accelerators. Check the hardware, parallel settings, context length and token counts before comparing results. These charts alone cannot tell you whether DeepInfer will be faster than vLLM or SGLang for your workload.

For a useful comparison, use the same checkpoint and output-quality checks. Measure how long users wait for the first token and subsequent tokens, how many output tokens are accepted, and how much compute the run uses.

Read official benchmark and validation notes

Where do DeepInfer and Recipe fit in an API?

DeepInfer runs the model. Recipe converts API requests into model inputs and parses the output into responses. To offer an API, you still need to connect these components and provide HTTP serving and tool execution.

  1. Request and response conversion. Recipe provides adapters for Chat Completions, Messages and Responses.
  2. Model execution. Connect an actual inference backend; Recipe's example servers use mock inference.
  3. Online serving. Complete the cache infrastructure, stage routing and load balancing needed for the service.

If you want to call a hosted DeepSeek model, start with the API documentation directory. Our in-stock Coding Plans are a separate access option from self-hosting DeepInfer.

DeepInfer questions

What is DeepSeek DeepInfer?

DeepInfer is DeepSeek's open-source inference framework. The public release provides an offline engine for DeepSeek-V4.1-Flash, with scheduling, sampling, parallel execution and optimized kernels.

Can DeepInfer run on a laptop, Windows or a consumer GPU?

The README lists Linux, NVIDIA SM100 and Huawei Ascend 950. It does not list Windows, Macs or consumer GPUs. Check the hardware guide before choosing a machine.

Does installing DeepInfer start an OpenAI-compatible API?

No. The setup instructions run an offline example. Recipe handles protocol conversion, but you must connect a real model backend and provide HTTP serving and tool execution. An online service also needs caching, routing between serving stages and load balancing.

Is DeepInfer faster than vLLM or SGLang?

The published charts do not answer that comparison. You would need to test the frameworks with the same checkpoint, hardware, context length, concurrency and quality settings, using the same method to count accepted output tokens.