{
  "id": 3552296,
  "title": "Deploying DeepSeek R1 Reasoning LLM Using SGLang",
  "url": "https://urgent.news/2026/08/26/deploying-deepseek-r1-reasoning-llm-using-sglang",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-26T17:24:34.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/vultr/deploying-deepseek-r1-reasoning-llm-using-sglang-54a5"
  },
  "original_language": "en",
  "account": "DeepSeek R1 is a reasoning model specialized in math, coding, and logical inference. It was developed by reinforcement learning, with a cold-start phase to ensure coherent and concise responses. This guide explains how to deploy DeepSeek R1 using SGLang, a framework built for large language models.\n\nThe deployment process requires an AMD Instinct MI300X GPU server with ample VRAM, as the model is quite large. Before starting, install the Hugging Face Command Line Interface (CLI) and initiate the model download in the background. This download can take considerable time due to the model's size. Afterward, clone the SGLang repository and build the ROCm container, which can also take up to 30 minutes. Some users may encounter a build error related to GnuTLS; if this occurs, add specific lines to the Dockerfile.rocm to increase buffer sizes for HTTP connections.\n\nOnce the container is built and running, the inference server can be launched using Docker. The server will access the GPU device, use mounted caches, allocate 16GB of shared memory, and employ tensor parallelism across 8 GPUs, serving on port 30000. To test the model, send an HTTP request to the server's local IP address and port. The request should include the model name, the user's message (in this case, a request to walk through solving a system of two linear equations), and a specified temperature for the model's creativity.\n\nFollowing successful deployment and testing, optional steps include exposing the server externally through a reverse proxy with TLS for secure access and tuning the tensor parallelism (--tp) based on available GPU resources. Comparing DeepSeek R1's chain-of-thought responses with those of a non-reasoning model can demonstrate the unique capabilities of the reasoning-focused model. For comprehensive instructions, refer to the original guide on Vultr Docs.",
  "summary": "DeepSeek R1 is a first-generation reasoning model tuned for math, coding, and logical reasoning — reinforcement learning with a cold-start phase for readability and coherence, minimizing repetition and language mixing. This guide deploys it via SGLang in a ROCm-supported container on an AMD Instinct MI300X GPU server, then verifies inference over HTTP. Prerequisites: access to an AMD Instinct…",
  "key_points": [
    "DeepSeek R1 specializes in math, coding, and logical inference",
    "Deploy using SGLang on AMD Instinct MI300X GPU server",
    "Test by sending HTTP request to server's local IP and port"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}