RRepoGEO

REPOGEO REPORT · LITE

huggingface/optimum-nvidia

Default branch main · commit 923dfd74 · scanned 5/16/2026, 5:51:38 PM

GitHub: 1,033 stars · 103 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
30 /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
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 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
    Accelerate Hugging Face Transformer model inference on NVIDIA GPUs with TensorRT-LLM and Triton, achieving significant speedups with minimal code changes.
  • mediumreadme#2
    Refine the README's opening statement for explicit AI understanding

    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 Transformer models on NVIDIA GPUs. It integrates NVIDIA's TensorRT-LLM and Triton Inference Server to accelerate large language models (LLMs) like LLaMA 2, achieving up to 28x faster inference with minimal code changes.

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
NVIDIA TensorRT-LLM
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA TensorRT-LLM · recommended 1×
  2. vLLM · recommended 1×
  3. DeepSpeed-MII · recommended 1×
  4. Hugging Face Optimum with ONNX Runtime · recommended 1×
  5. Hugging Face Optimum with TorchInductor · recommended 1×
  • CATEGORY QUERY
    How to significantly speed up large language model inference on NVIDIA GPUs?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT-LLM
    2. vLLM
    3. DeepSpeed-MII
    4. Hugging Face Optimum with ONNX Runtime
    5. Hugging Face Optimum with TorchInductor
    6. FasterTransformer (NVIDIA)
    7. OpenVINO (Intel)
    8. Custom CUDA Kernels
    9. Triton (OpenAI)

    AI recommended 9 alternatives but never named huggingface/optimum-nvidia. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools optimize existing transformer model inference performance on NVIDIA platforms?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. NVIDIA Triton Inference Server
    3. ONNX Runtime
    4. PyTorch
    5. JAX
    6. DeepSpeed

    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 named huggingface/optimum-nvidia explicitly

    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?

Embed your GEO score

Drop this badge into the README of huggingface/optimum-nvidia. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/huggingface/optimum-nvidia.svg)](https://repogeo.com/en/r/huggingface/optimum-nvidia)
HTML
<a href="https://repogeo.com/en/r/huggingface/optimum-nvidia"><img src="https://repogeo.com/badge/huggingface/optimum-nvidia.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

huggingface/optimum-nvidia — 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