REPOGEO REPORT · LITE
mli/transformers-benchmarks
Default branch main · commit 3370b2b3 · scanned 5/30/2026, 5:33:04 PM
GitHub: 912 stars · 118 forks
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 mli/transformers-benchmarks, 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.
- hightopics#1Add relevant topics to the repository
Why:
COPY-PASTE FIXgpu-benchmarks, transformer-performance, deep-learning-benchmarks, machine-learning-performance, llm-training, teraflops, gpu-comparison, performance-estimation
- highreadme#2Refine the README's opening sentence to emphasize benchmarking and estimation
Why:
CURRENTWe benchmark real TeraFLOPS that training Transformer models can achieve on various GPUs, including single GPU, multi-GPUs, and multi-machines. It helps you to estimate how many machine times you need to train your large-scale Transformer models.
COPY-PASTE FIXThis repository provides **real-world TeraFLOPS benchmarks** for training Transformer models across various GPUs (single, multi-GPU, multi-machine setups). Use these comprehensive performance comparisons to accurately **estimate training times** and optimize resource allocation for your large-scale Transformer models.
- mediumhomepage#3Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIXhttps://github.com/mli/transformers-benchmarks
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.
- NVIDIA Deep Learning Performance Documentation & Benchmarks · recommended 1×
- MLPerf Benchmarks · recommended 1×
- Hugging Face Transformers Library · recommended 1×
- accelerate · recommended 1×
- DeepSpeed · recommended 1×
- CATEGORY QUERYHow to estimate training time for large language models on different GPUs?you: not recommendedAI recommended (in order):
- NVIDIA Deep Learning Performance Documentation & Benchmarks
- MLPerf Benchmarks
- Hugging Face Transformers Library
- accelerate
- DeepSpeed
- AWS EC2 Instance Types & Pricing
- Google Cloud AI Platform / Vertex AI
- PyTorch Profiler
- NVIDIA Nsight Systems
AI recommended 9 alternatives but never named mli/transformers-benchmarks. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat are the real-world performance benchmarks for training deep learning models on various GPUs?you: not recommendedAI recommended (in order):
- MLPerf
- Phoronix
- Lambda Labs Blog
- Papers With Code
- AnandTech
- TechPowerUp
AI recommended 6 alternatives but never named mli/transformers-benchmarks. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesswarn
Suggestion:
- README presencepass
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 mli/transformers-benchmarks?passAI did not name mli/transformers-benchmarks — 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 mli/transformers-benchmarks in production, what risks or prerequisites should they evaluate first?passAI named mli/transformers-benchmarks 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 mli/transformers-benchmarks solve, and who is the primary audience?passAI did not name mli/transformers-benchmarks — 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?
Embed your GEO score
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mli/transformers-benchmarks — 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