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Developer runs 122B Qwen LLM locally on AMD MI300X GPU

By PulseAugur Editorial · [1 sources] ·

A developer has successfully deployed the Qwen3.5-122B large language model locally on AMD MI300X GPU infrastructure. This setup utilizes vLLM and SGLang for inference, enabling an autonomous agent built with OpenClaw. The deployment highlights the feasibility of self-hosting frontier-scale models, offering benefits like data sovereignty, cost predictability, and freedom from API rate limits, all within a single 192GB MI300X GPU. AI

IMPACT Enables self-hosting of large models, offering data sovereignty and cost control for AI applications.

RANK_REASON The article details the technical setup and learnings of running a specific large language model locally on particular hardware, which falls under tooling and infrastructure rather than a new model release or significant industry event.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Developer runs 122B Qwen LLM locally on AMD MI300X GPU

COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Yogi ·

    Running a 122B Parameter Agent Locally on AMD MI300X — What I Learned

    <p>Self-hosting Qwen3.5-122B with vLLM and SGLang on AMD GPU infrastructure, then building a fully autonomous agent on top of it</p> <h2> Introduction </h2> <p>Most conversations about running large language models locally stop at 7B or 13B parameter models on consumer GPUs. That…

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