REPOGEO REPORT · LITE
scrya-com/rotorquant
Default branch main · commit fcd76768 · scanned 6/30/2026, 6:27:42 AM
GitHub: 1,025 stars · 87 forks
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.
3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).
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 scrya-com/rotorquant, 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.
- highlicense#1Add a LICENSE file to the repository root
Why:
CURRENT(no LICENSE file detected — the repo has no recognizable license)
COPY-PASTE FIXCreate a LICENSE file in the repository root containing the text of a standard open-source license like MIT or Apache-2.0.
- hightopics#2Add relevant topics to the repository
Why:
CURRENT(none)
COPY-PASTE FIXllm, kv-cache, quantization, inference, deep-learning, machine-learning, transformer, performance, compression, llama-cpp
- mediumreadme#3Add a concise problem statement to the README's opening
Why:
CURRENT# RotorQuant: KV Cache Compression for LLMs Drop-in KV cache quantization that **bypasses the butterfly network** using block-diagonal rotations.
COPY-PASTE FIX# RotorQuant: KV Cache Compression for LLMs **For LLM inference, optimizing KV cache memory and speed is critical.** RotorQuant offers drop-in KV cache quantization that **bypasses the butterfly network** using block-diagonal rotations.
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/TensorRT-LLM · recommended 1×
- ggerganov/llama.cpp · recommended 1×
- huggingface/optimum · recommended 1×
- TimDettmers/bitsandbytes · recommended 1×
- PanQiWei/AutoGPTQ · recommended 1×
- CATEGORY QUERYHow to improve LLM inference speed and reduce KV cache memory usage efficiently?you: not recommendedAI recommended (in order):
- NVIDIA TensorRT-LLM (NVIDIA/TensorRT-LLM)
- llama.cpp (ggerganov/llama.cpp)
- Hugging Face Optimum (huggingface/optimum)
- bitsandbytes (TimDettmers/bitsandbytes)
- AutoGPTQ (PanQiWei/AutoGPTQ)
- Intel OpenVINO (openvinotoolkit/openvino)
- Hugging Face Transformers (huggingface/transformers)
- vLLM (vllm-project/vllm)
- Llama 2 70B
- Falcon
- Triton Inference Server (triton-inference-server/server)
- FlashAttention-2 (Dao-AILab/flash-attention)
- PyTorch 2.x (pytorch/pytorch)
AI recommended 13 alternatives but never named scrya-com/rotorquant. This is the gap to close.
Show full AI answer
- CATEGORY QUERYSeeking better KV cache quantization methods for LLMs, focusing on faster prefill and decode.you: not recommendedAI recommended (in order):
- AWQ (Activation-aware Weight Quantization)
- GPTQ (Generative Pre-trained Transformer Quantization)
- SmoothQuant
- HQQ (Half-Quadratic Quantization)
- OFT (Optimal Feature Quantization)
- SpQR (Sparsity-aware Quantization for LLMs)
AI recommended 6 alternatives but never named scrya-com/rotorquant. 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 scrya-com/rotorquant?passAI named scrya-com/rotorquant explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts scrya-com/rotorquant in production, what risks or prerequisites should they evaluate first?passAI named scrya-com/rotorquant 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 scrya-com/rotorquant solve, and who is the primary audience?passAI named scrya-com/rotorquant 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 scrya-com/rotorquant. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.
Live preview[](https://repogeo.com/en/r/scrya-com/rotorquant)<a href="https://repogeo.com/en/r/scrya-com/rotorquant"><img src="https://repogeo.com/badge/scrya-com/rotorquant.svg" alt="RepoGEO" /></a>Subscribe to Pro for deep diagnoses
scrya-com/rotorquant — 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