RRepoGEO

REPOGEO REPORT · LITE

huggingface/optimum-nvidia

Default branch main · commit 923dfd74 · scanned 6/27/2026, 3:26:43 PM

GitHub: 1,037 stars · 104 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)

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

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 huggingface/optimum-nvidia, 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

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

OVERALL DIRECTION
  • highabout#1
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    Optimized inference for Hugging Face models on NVIDIA GPUs, leveraging technologies like TensorRT-LLM for significant speedups, especially for large language models.
  • mediumreadme#2
    Refine the README's opening sentence to emphasize LLM acceleration

    Why:

    CURRENT
    Optimum-NVIDIA delivers the best inference performance on the NVIDIA platform through Hugging Face. Run LLaMA 2 at 1,200 tokens/second (up to 28x faster than the framework) by changing just a single line in your existing transformers code.
    COPY-PASTE FIX
    Optimum-NVIDIA delivers the best inference performance for Hugging Face models, especially large language models (LLMs), on the NVIDIA platform. Run LLaMA 2 at 1,200 tokens/second (up to 28x faster than the framework) by changing just a single line in your existing transformers code, leveraging NVIDIA GPUs and TensorRT-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 huggingface/optimum-nvidia
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/optimum
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/optimum · recommended 2×
  2. pytorch/pytorch · recommended 2×
  3. NVIDIA TensorRT-LLM · recommended 1×
  4. vllm-project/vllm · recommended 1×
  5. microsoft/onnxruntime · recommended 1×
  • CATEGORY QUERY
    How to accelerate large language model inference on NVIDIA GPUs?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT-LLM
    2. vLLM (vllm-project/vllm)
    3. Hugging Face Optimum (huggingface/optimum)
    4. BetterTransformer (huggingface/optimum)
    5. ONNX Runtime (microsoft/onnxruntime)
    6. DeepSpeed-MII (microsoft/DeepSpeed)
    7. PyTorch (pytorch/pytorch)
    8. torch.compile (pytorch/pytorch)
    9. FlashAttention-2 (Dao-AILab/flash-attention)
    10. Triton Inference Server (triton-inference-server/server)

    AI recommended 10 alternatives but never named huggingface/optimum-nvidia. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools optimize transformer model performance with minimal code changes?
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed
    2. PyTorch FSDP
    3. Hugging Face Accelerate
    4. NVIDIA Apex
    5. ONNX Runtime
    6. TensorRT

    AI recommended 6 alternatives but never named huggingface/optimum-nvidia. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 huggingface/optimum-nvidia?
    pass
    AI did not name huggingface/optimum-nvidia — 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?

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

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

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huggingface/optimum-nvidia — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite