RRepoGEO

REPOGEO REPORT · LITE

deepseek-ai/DeepGEMM

Default branch main · commit 714dd1a4 · scanned 5/17/2026, 1:37:02 AM

GitHub: 7,261 stars · 983 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 deepseek-ai/DeepGEMM, 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
  • hightopics#1
    Add specific topics for LLM, GEMM, and CUDA kernels

    Why:

    COPY-PASTE FIX
    llm, deep-learning, gpu, cuda, gemm, fp8, fp4, bf16, moe, tensor-cores, kernel-library, nvidia
  • highreadme#2
    Clarify the README's H1 to emphasize LLM-specific optimizations

    Why:

    CURRENT
    # DeepGEMM
    COPY-PASTE FIX
    # DeepGEMM: High-Performance CUDA Kernels for LLM Quantization (FP8/FP4) and MoE
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    (A relevant project or organization URL, e.g., https://deepseek-ai.com/)

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 deepseek-ai/DeepGEMM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA Tensor Cores
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA Tensor Cores · recommended 1×
  2. cuBLASLt · recommended 1×
  3. cuDNN · recommended 1×
  4. pytorch/pytorch · recommended 1×
  5. tensorflow/tensorflow · recommended 1×
  • CATEGORY QUERY
    How to optimize matrix multiplication for large language models using FP8?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Tensor Cores
    2. cuBLASLt
    3. cuDNN
    4. PyTorch (pytorch/pytorch)
    5. TensorFlow (tensorflow/tensorflow)
    6. torch.compile
    7. XLA (openxla/xla)
    8. Intel AMX
    9. oneMKL (oneapi-src/oneMKL)
    10. Intel Extension for PyTorch (intel/intel-extension-for-pytorch)
    11. AMD CDNA Architecture
    12. ROCm (ROCm/ROCm)
    13. rocBLAS (ROCm/rocBLAS)
    14. Google TPU
    15. JAX (google/jax)
    16. OpenAI Triton (openai/triton)
    17. Apache TVM (apache/tvm)
    18. CUDA
    19. HIP (ROCm/HIP)
    20. OpenCL

    AI recommended 20 alternatives but never named deepseek-ai/DeepGEMM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best high-performance CUDA kernel libraries for MoE and low-precision GEMMs?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA cuBLASLt
    2. NVIDIA cuDNN
    3. NVIDIA FasterTransformer
    4. NVIDIA Triton Inference Server
    5. PyTorch
    6. TensorFlow
    7. OpenAI Triton

    AI recommended 7 alternatives but never named deepseek-ai/DeepGEMM. 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 deepseek-ai/DeepGEMM?
    pass
    AI named deepseek-ai/DeepGEMM explicitly

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

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

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

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deepseek-ai/DeepGEMM — 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