REPOGEO REPORT · LITE
yandex/YaFSDP
Default branch main · commit 7e128205 · scanned 6/6/2026, 1:57:39 PM
GitHub: 989 stars · 47 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 yandex/YaFSDP, 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 for LLM distributed training
Why:
COPY-PASTE FIXdistributed-training, large-language-models, llm-training, pytorch, fsdp, data-parallelism, deep-learning, transformer-models, gpu-acceleration
- highreadme#2Add a concise, benefit-oriented tagline to the README's opening
Why:
COPY-PASTE FIXYaFSDP is a high-performance Sharded Data Parallelism framework specifically engineered by Yandex to accelerate large language model (LLM) training on multi-GPU setups, offering significant speedups and memory efficiency over standard FSDP.
- mediumhomepage#3Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIXA URL pointing to the official project page, documentation, or a key introductory blog post (e.g., the Medium or Habr posts mentioned in the README).
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.
- DeepSpeed · recommended 2×
- PyTorch FSDP · recommended 2×
- Megatron-LM · recommended 2×
- Colossal-AI · recommended 2×
- FairScale · recommended 2×
- CATEGORY QUERYHow to efficiently train large language models across multiple GPUs with reduced memory overhead?you: not recommendedAI recommended (in order):
- DeepSpeed
- PyTorch FSDP
- Megatron-LM
- Accelerate
- Colossal-AI
- FairScale
AI recommended 6 alternatives but never named yandex/YaFSDP. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat are the best sharded data parallelism frameworks for accelerating transformer model training?you: not recommendedAI recommended (in order):
- DeepSpeed
- FairScale
- PyTorch FSDP
- Megatron-LM
- Colossal-AI
AI recommended 5 alternatives but never named yandex/YaFSDP. 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 yandex/YaFSDP?passAI named yandex/YaFSDP explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts yandex/YaFSDP in production, what risks or prerequisites should they evaluate first?passAI named yandex/YaFSDP 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 yandex/YaFSDP solve, and who is the primary audience?passAI named yandex/YaFSDP 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 yandex/YaFSDP. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.
[](https://repogeo.com/en/r/yandex/YaFSDP)<a href="https://repogeo.com/en/r/yandex/YaFSDP"><img src="https://repogeo.com/badge/yandex/YaFSDP.svg" alt="RepoGEO" /></a>Subscribe to Pro for deep diagnoses
yandex/YaFSDP — 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