RRepoGEO

REPOGEO REPORT · LITE

intel/ipex-llm

Default branch main · commit de6bce27 · scanned 6/21/2026, 9:16:31 AM

GitHub: 8,843 stars · 1,428 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
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 intel/ipex-llm, 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 the project's core identity as an LLM acceleration library

    Why:

    CURRENT
    The current README starts with the ARCHIVED notice, then "# 💫 Intel® LLM Library for PyTorch*".
    COPY-PASTE FIX
    Insert this sentence after the "THIS PROJECT IS ARCHIVED" block and before the existing "# 💫 Intel® LLM Library for PyTorch*" heading:
    "This archived project, **Intel® LLM Library for PyTorch* (IPEX-LLM)**, provides comprehensive examples and optimizations for accelerating Large Language Model (LLM) inference and finetuning on Intel XPUs (iGPU, NPU, Arc, Flex, Max), demonstrating integration with popular frameworks like HuggingFace, LangChain, and LlamaIndex."
  • mediumtopics#2
    Expand topics for better specificity

    Why:

    CURRENT
    gpu, llm, pytorch, transformers
    COPY-PASTE FIX
    intel-xpu, llm-acceleration, llm-inference, llm-finetuning, pytorch-llm, huggingface-transformers, langchain, llamaindex, ollama, vllm, deepspeed, axolotl
  • lowhomepage#3
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    https://github.com/intel/ipex-llm

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 intel/ipex-llm
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenVINO Toolkit
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenVINO Toolkit · recommended 1×
  2. Intel Extension for PyTorch (IPEX) · recommended 1×
  3. Intel Extension for TensorFlow · recommended 1×
  4. oneAPI Deep Neural Network Library (oneDNN) · recommended 1×
  5. Intel oneAPI Base Toolkit · recommended 1×
  • CATEGORY QUERY
    How to accelerate large language model inference and finetuning on Intel integrated or discrete GPUs?
    you: not recommended
    AI recommended (in order):
    1. OpenVINO Toolkit
    2. Intel Extension for PyTorch (IPEX)
    3. Intel Extension for TensorFlow
    4. oneAPI Deep Neural Network Library (oneDNN)
    5. Intel oneAPI Base Toolkit

    AI recommended 5 alternatives but never named intel/ipex-llm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What libraries optimize HuggingFace or LangChain LLM performance on Intel-based systems?
    you: not recommended
    AI recommended (in order):
    1. Intel Extension for PyTorch (IPEX) (intel/intel-extension-for-pytorch)
    2. OpenVINO Toolkit (openvinotoolkit/openvino)
    3. Intel Extension for Transformers (ITREX) (intel/intel-extension-for-transformers)
    4. DeepSpeed (microsoft/DeepSpeed)
    5. ONNX Runtime (microsoft/onnxruntime)
    6. PyTorch (pytorch/pytorch)

    AI recommended 6 alternatives but never named intel/ipex-llm. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 intel/ipex-llm?
    pass
    AI named intel/ipex-llm explicitly

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

  • If a team adopts intel/ipex-llm in production, what risks or prerequisites should they evaluate first?
    pass
    AI named intel/ipex-llm 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 intel/ipex-llm solve, and who is the primary audience?
    pass
    AI named intel/ipex-llm explicitly

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

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intel/ipex-llm — 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
intel/ipex-llm — RepoGEO report