RRepoGEO

REPOGEO REPORT · LITE

SemiAnalysisAI/InferenceX

Default branch main · commit 090ecbf7 · scanned 6/28/2026, 6:53:16 AM

GitHub: 1,158 stars · 208 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
40 /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
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 SemiAnalysisAI/InferenceX, 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
    Strengthen README's opening sentence to clarify its unique platform nature

    Why:

    CURRENT
    InferenceX™ (formerly InferenceMAX) is an inference performance research platform dedicated to continually analyzing & benchmarking the world’s most popular op
    COPY-PASTE FIX
    InferenceX™ (formerly InferenceMAX) is the leading open-source research platform for **continuous, comparative benchmarking of large language model (LLM) inference performance** across diverse hardware accelerators and software stacks.
  • mediumtopics#2
    Add more specific topics to emphasize continuous LLM benchmarking and comparison

    Why:

    CURRENT
    ai, amd, benchmark, cuda, deepseek, gb200, gb300, glm, kimi, llm, mi355x, minimax, nvidia, pytorch, rocm, sglang, vllm
    COPY-PASTE FIX
    ai, amd, benchmark, cuda, deepseek, gb200, gb300, glm, kimi, llm, mi355x, minimax, nvidia, pytorch, rocm, sglang, vllm, llm-inference, continuous-benchmarking, hardware-benchmarking, gpu-comparison, inference-platform
  • lowcomparison#3
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    ## Comparison with Alternatives

Unlike low-level libraries such as NVIDIA TensorRT, DeepSpeed, or PyTorch Profiler, InferenceX is a comprehensive, continuous benchmarking platform. We provide real-world, comparative performance data for LLM inference across a wide range of hardware and software stacks, rather than focusing on optimizing individual model deployments or profiling specific operations.

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 SemiAnalysisAI/InferenceX
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorRT
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorRT · recommended 2×
  2. microsoft/DeepSpeed · recommended 2×
  3. huggingface/optimum · recommended 2×
  4. pytorch/pytorch · recommended 2×
  5. NVIDIA DLProf · recommended 1×
  • CATEGORY QUERY
    What tools can help benchmark large language model inference performance on various GPUs?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA DLProf
    2. TensorRT
    3. DeepSpeed (microsoft/DeepSpeed)
    4. Hugging Face Optimum (huggingface/optimum)
    5. PyTorch Profiler (pytorch/pytorch)
    6. `perf_analyzer` (triton-inference-server/perf_analyzer)
    7. `torch.utils.benchmark` (pytorch/pytorch)

    AI recommended 7 alternatives but never named SemiAnalysisAI/InferenceX. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a platform to compare continuous inference performance of various LLMs on different accelerators.
    you: not recommended
    AI recommended (in order):
    1. MLPerf Inference
    2. NVIDIA Triton Inference Server (triton-inference-server/server)
    3. OpenVINO Toolkit (openvinotoolkit/openvino)
    4. DeepSpeed (microsoft/DeepSpeed)
    5. TensorRT
    6. Hugging Face Optimum (huggingface/optimum)

    AI recommended 6 alternatives but never named SemiAnalysisAI/InferenceX. 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 SemiAnalysisAI/InferenceX?
    pass
    AI named SemiAnalysisAI/InferenceX explicitly

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

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

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

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SemiAnalysisAI/InferenceX — 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