RRepoGEO

REPOGEO REPORT · LITE

Infrasys-AI/AIInfra

Default branch main · commit b97c0385 · scanned 5/20/2026, 11:18:43 AM

GitHub: 7,064 stars · 912 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
33 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface Infrasys-AI/AIInfra, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README H1 to clarify project as a course/guide

    Why:

    CURRENT
    # AIInfra

文字课程内容正在一节节补充更新，尽可能抽空继续更新正在 :octocat: AIInfra，希望您多多鼓励和参与进来！！！
    COPY-PASTE FIX
    # AIInfra：大模型AI基础设施全栈设计与优化开源课程

AIInfra 是一个系统性的开源课程项目，旨在深入探讨和学习如何设计、构建和优化面向大模型的全栈AI基础设施，涵盖从底层芯片硬件到上层软件栈的系统性知识和实践。
  • mediumtopics#2
    Expand topics to include educational and specific AI infrastructure keywords

    Why:

    CURRENT
    aiinfra, aisystem
    COPY-PASTE FIX
    aiinfra, aisystem, ai-infrastructure-design, large-language-models, llm-training, distributed-ai, system-design, ai-optimization, course, education, deep-learning-systems
  • lowreadme#3
    Add a section clarifying project's relationship to hardware/tools

    Why:

    COPY-PASTE FIX
    ## 与现有工具和平台的关系

AIInfra 本质上是一个知识体系和实践指南，旨在教授如何有效设计、构建和优化大模型AI基础设施。它并非具体的硬件产品（如 NVIDIA GPU, Google TPUs）或云服务（如 AWS EC2, Azure ND），也不是直接的软件工具（如 Kubernetes, PyTorch Distributed）。相反，本课程旨在帮助您理解并利用这些底层资源和上层工具，以构建和管理您自己的高性能AI系统。

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface Infrasys-AI/AIInfra
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA DGX Systems
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA DGX Systems · recommended 1×
  2. NVIDIA A100/H100 GPUs · recommended 1×
  3. Google Cloud TPUs · recommended 1×
  4. AWS EC2 P4d/P5 Instances · recommended 1×
  5. Azure ND A100 v4-series / ND H100 v5-series · recommended 1×
  • CATEGORY QUERY
    How to design and optimize full-stack AI infrastructure for large model training?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA DGX Systems
    2. NVIDIA A100/H100 GPUs
    3. Google Cloud TPUs
    4. AWS EC2 P4d/P5 Instances
    5. Azure ND A100 v4-series / ND H100 v5-series
    6. NVIDIA InfiniBand
    7. RoCEv2
    8. NVIDIA NVLink
    9. NVIDIA GPUDirect Storage
    10. BeeGFS
    11. Lustre
    12. Pure Storage FlashBlade/FlashArray
    13. DirectFlash Fabric
    14. NetApp ONTAP AI
    15. Amazon FSx for Lustre
    16. Amazon FSx for NetApp ONTAP
    17. Google Cloud Filestore
    18. Kubernetes
    19. NVIDIA GPU Operator
    20. Slurm Workload Manager
    21. NVIDIA Base Command Platform
    22. Ray
    23. PyTorch
    24. TensorFlow
    25. Keras
    26. NVIDIA CUDA Toolkit
    27. NVIDIA cuDNN
    28. NVIDIA NCCL
    29. Hugging Face Transformers
    30. DeepSpeed
    31. FSDP (Fully Sharded Data Parallel)
    32. Prometheus
    33. Grafana
    34. NVIDIA DCGM
    35. Elasticsearch
    36. Logstash
    37. Kibana
    38. Weights & Biases
    39. MLflow
    40. NVIDIA Nsight Systems

    AI recommended 40 alternatives but never named Infrasys-AI/AIInfra. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best practices for building an AI system for distributed large language model training?
    you: not recommended
    AI recommended (in order):
    1. Kubernetes (kubernetes/kubernetes)
    2. Kubeflow (kubeflow/kubeflow)
    3. Slurm (SchedMD/slurm)
    4. PyTorch Distributed (torch.distributed) (pytorch/pytorch)
    5. TensorFlow Distributed (tf.distribute) (tensorflow/tensorflow)
    6. DeepSpeed (microsoft/DeepSpeed)
    7. Megatron-LM (NVIDIA/Megatron-LM)
    8. Apache Arrow (apache/arrow)
    9. Parquet
    10. Hugging Face Datasets (huggingface/datasets)
    11. NVIDIA DALI (Data Loading Library) (NVIDIA/DALI)
    12. Weights & Biases (W&B) (wandb/wandb)
    13. Prometheus (prometheus/prometheus)
    14. Grafana (grafana/grafana)
    15. TensorBoard (tensorflow/tensorboard)
    16. NVIDIA GPUs (A100, H100)
    17. NVLink
    18. NVSwitch
    19. InfiniBand
    20. RoCE
    21. Git (git/git)
    22. GitHub
    23. GitLab (gitlabhq/gitlabhq)
    24. Docker (docker/docker-ce)
    25. Singularity (apptainer/apptainer)
    26. MLflow (mlflow/mlflow)
    27. Ray Tune (ray-project/ray)
    28. Optuna (optuna/optuna)

    AI recommended 28 alternatives but never named Infrasys-AI/AIInfra. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of Infrasys-AI/AIInfra?
    pass
    AI named Infrasys-AI/AIInfra explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts Infrasys-AI/AIInfra in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Infrasys-AI/AIInfra explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo Infrasys-AI/AIInfra solve, and who is the primary audience?
    pass
    AI did not name Infrasys-AI/AIInfra — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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Infrasys-AI/AIInfra — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite