A high-performance inference engine for LLM, VLM, DiT and REC models, optimized for diverse AI accelerators. It is hosted in OpenAtom Foundation.
xLLM is an inference engine, meaning software that runs trained AI models to produce outputs from inputs, for large language, vision-language, diffusion, and recommendation models on different AI accelerators. Organizations use it to deploy these models with high-throughput and low-latency inference. The catalogue entries provide skills and instructions for working with xLLM.
Skill Claude CodeCodex
Add or update xLLM unit tests in the repository. Use when Codex needs to create a new C++/CUDA/NPU/MLU unit test, place a test under tests/, wire it into CMake with cctest, update an existing test target, choose platform gates, or validate test naming and dependencies against current xLLM test conventions.
Skill Claude CodeCodex
Review code changes for quality, security, performance, and correctness following project-specific standards. Use when reviewing pull requests, examining git diffs, or when the user asks for a code review. This skill should be used proactively — when the user asks for a review without specifying commits, automatically…
Skill Claude CodeCodex
Use when the task involves Git operations for the public xLLM repository, including choosing branch or tag names, preparing commits and pull requests, backporting fixes, checking repo-specific review expectations, or drafting commit messages from actual diffs.
Skill Claude CodeCodex
Use when the user wants to add, modify, debug, or review an xLLM TileLang Ascend kernel or specialization, including Python kernel definitions, generated Ascend-C source, runtime wrapper dispatch, TileLang CMake wiring, and NPU tests.
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: