REPOGEO REPORT · LITE

intel/neural-compressor

Default branch main · commit 6419107f · scanned 6/22/2026, 12:11:21 PM

GitHub: 2,663 stars · 309 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

33 /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

2 / 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 intel/neural-compressor, 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

Reposition README H3 to specify LLM quantization focus

Why:

CURRENT

An open-source Python library supporting popular model compression techniques on mainstream deep learning frameworks (PyTorch, TensorFlow, and JAX)

COPY-PASTE FIX

Intel® Neural Compressor is a leading open-source Python library for **state-of-the-art low-bit LLM quantization and sparsity** (INT8/FP8/MXFP8/INT4/MXFP4/NVFP4), offering advanced model compression techniques across PyTorch, TensorFlow, and ONNX Runtime.

mediumcomparison#2

Add a 'Comparison with Alternatives' section to README

Why:

COPY-PASTE FIX

Add a new section titled 'Why Intel® Neural Compressor?' or 'Comparison with Alternatives' to the README, explicitly outlining its advantages for low-bit LLM quantization and Intel hardware optimization compared to general frameworks like ONNX Runtime, PyTorch, or TensorRT.

lowreadme#3

Increase prominence of Intel hardware optimization in README

Why:

COPY-PASTE FIX

Relocate the existing detailed hardware support paragraph (starting 'Support a wide range of Intel hardware...') to appear immediately after the main project description, emphasizing its core differentiator.

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 intel/neural-compressor

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

ONNX Runtime

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

ONNX Runtime · recommended 2×
NVIDIA TensorRT · recommended 2×
Hugging Face Optimum · recommended 1×
Intel OpenVINO · recommended 1×
PyTorch · recommended 1×

CATEGORY QUERY
How to reduce the memory footprint and inference latency of large language models?
you: not recommended
AI recommended (in order):
1. Hugging Face Optimum
2. ONNX Runtime
3. Intel OpenVINO
4. NVIDIA TensorRT
5. PyTorch
6. bitsandbytes
7. Neural Magic DeepSparse
8. Hugging Face Transformers
9. TensorFlow
10. Mistral 7B
11. Phi-2
12. TinyLlama
13. DistilBERT
14. DistilRoBERTa
15. DeepSpeed
AI recommended 15 alternatives but never named intel/neural-compressor. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a library for efficient low-precision quantization and sparsity techniques across deep learning frameworks.
you: not recommended
AI recommended (in order):
1. NVIDIA TensorRT
2. OpenVINO Toolkit
3. ONNX Runtime
4. PyTorch Quantization APIs
5. TensorFlow Model Optimization Toolkit
6. DeepSparse
7. TVM
AI recommended 7 alternatives but never named intel/neural-compressor. 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 intel/neural-compressor?
pass
AI named intel/neural-compressor explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts intel/neural-compressor in production, what risks or prerequisites should they evaluate first?
pass
AI named intel/neural-compressor 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 intel/neural-compressor solve, and who is the primary audience?
pass
AI did not name intel/neural-compressor — 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?

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MARKDOWN (README)

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intel/neural-compressor — 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