RRepoGEO

REPOGEO REPORT · LITE

mit-han-lab/llm-awq

Default branch main · commit d6e797a4 · scanned 6/24/2026, 7:58:43 AM

GitHub: 3,572 stars · 316 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
28 /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
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 mit-han-lab/llm-awq, 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 relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm, quantization, deep-learning, machine-learning, ai, inference, compression, acceleration, pytorch, cuda, edge-devices, multi-modal, llm-quantization, awq
  • highreadme#2
    Explicitly state the repository's role as the official AWQ implementation in the README

    Why:

    CURRENT
    # AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
    COPY-PASTE FIX
    Add the following sentence immediately after the title: 'This repository provides the official and reference implementation of the AWQ (Activation-aware Weight Quantization) method, recognized with the MLSys 2024 Best Paper Award.'
  • mediumcomparison#3
    Add a comparison section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, e.g., 'Comparison with other Quantization Methods', detailing how AWQ (as implemented in this repository) compares to alternatives like GPTQ in terms of accuracy, speed, and supported models.

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 mit-han-lab/llm-awq
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PanQiWei/AutoGPTQ
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PanQiWei/AutoGPTQ · recommended 1×
  2. mit-han-lab/awq · recommended 1×
  3. TimDettmers/bitsandbytes · recommended 1×
  4. microsoft/onnxruntime · recommended 1×
  5. NVIDIA/TensorRT-LLM · recommended 1×
  • CATEGORY QUERY
    How can I quantize large language models for faster, memory-efficient inference on edge devices?
    you: not recommended
    AI recommended (in order):
    1. GPTQ (PanQiWei/AutoGPTQ)
    2. AWQ (mit-han-lab/awq)
    3. bitsandbytes (TimDettmers/bitsandbytes)
    4. ONNX Runtime (microsoft/onnxruntime)
    5. TensorRT-LLM (NVIDIA/TensorRT-LLM)
    6. OpenVINO Toolkit (openvinotoolkit/openvino)
    7. Apache TVM (apache/tvm)

    AI recommended 7 alternatives but never named mit-han-lab/llm-awq. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for methods to accelerate instruction-tuned and multi-modal LLM inference with low-bit quantization.
    you: not recommended
    AI recommended (in order):
    1. AWQ
    2. GPTQ
    3. bitsandbytes
    4. AutoGPTQ
    5. llama.cpp
    6. ONNX Runtime
    7. TensorRT-LLM

    AI recommended 7 alternatives but never named mit-han-lab/llm-awq. 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 mit-han-lab/llm-awq?
    pass
    AI did not name mit-han-lab/llm-awq — 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 mit-han-lab/llm-awq in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mit-han-lab/llm-awq 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 mit-han-lab/llm-awq solve, and who is the primary audience?
    pass
    AI named mit-han-lab/llm-awq explicitly

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

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mit-han-lab/llm-awq — 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