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AutoGPTQ
STEM & AI Researchers Arena Rank #144

AutoGPTQ

AutoGPTQ/AutoGPTQ · Author: @AutoGPTQ
Arena ELO
1298
±18
Total Stars
5.1k
+11.2% w/w
Monthly Traffic
440k/mo
0.5x vs median
Search Demand
76,000/mo
+112% YoY

12-Month Adoption & Star Velocity +11.2% w/w

Empirical star trajectory for AutoGPTQ vs Category Median benchmark. Hover along points to inspect exact monthly stats.

AutoGPTQ Category Median
18k 9k 183 NovDecJanFebMarAprMayJunJulAugSepOct Nov · 183 vs 4.4k med This Skill: 183 -4.2k vs Median Dec · 247 vs 5.0k med This Skill: 247 -4.8k vs Median Jan · 334 vs 5.7k med This Skill: 334 -5.4k vs Median Feb · 452 vs 6.5k med This Skill: 452 -6.0k vs Median Mar · 612 vs 7.3k med This Skill: 612 -6.7k vs Median Apr · 827 vs 8.4k med This Skill: 827 -7.5k vs Median May · 1.1k vs 9.5k med This Skill: 1.1k -8.4k vs Median Jun · 1.5k vs 10.8k med This Skill: 1.5k -9.3k vs Median Jul · 2.0k vs 12.3k med This Skill: 2.0k -10.2k vs Median Aug · 2.8k vs 13.9k med This Skill: 2.8k -11.2k vs Median Sep · 3.7k vs 15.8k med This Skill: 3.7k -12.1k vs Median Oct · 5.1k vs 18.0k med This Skill: 5.1k -12.9k vs Median
GROWTH VELOCITY
+11.2%
2.0x vs category median
ARENA ELO SCORE
1298
+106 vs category median
WEB VISITS MOMENTUM
440k/mo
0.5x category median
LATENCY EFFICIENCY
11ms
3.5x faster execution

Ecosystem Adoption Thesis

Across verified open-source agentic tools, AutoGPTQ holds a position in the top percentile for developer retention and production velocity. Its weekly surge rate of +11.2% signals sustained real-world adoption rather than speculative hype.

Why Teams & Autonomous Agents Choose AutoGPTQ

An easy-to-use LLMs quantization package with user-friendly APIs, based on GPTQ algorithm.

Verified Real-World Production Workflow

Primary Implementation:

Compress Hugging Face foundation models to 4-bit and 3-bit weights for low-VRAM deployment.

Engine Stack & Dependencies:

Python, PyTorch, CUDA, Triton kernels, Marlin backend.

Target Persona & Role Fit

GPTQ Quantization Engineers

Engineered and benchmarked specifically for GPTQ Quantization Engineers demanding deterministic execution, low token overhead, and production reliability in agentic loops.

Production Blueprint & Installation

git clone https://github.com/AutoGPTQ/AutoGPTQ

Technical Specification (ASD-STE100)

An easy-to-use LLMs quantization package with user-friendly APIs, based on GPTQ algorithm.
Architecture: Python, PyTorch, CUDA, Triton kernels, Marlin backend.

Domain Tags & Keywords

#gptq#post-training-quantization#low-vram#compression

Compute Efficiency Profile

P95 EXECUTION LATENCY
11ms
3.5x faster than median
TOKEN EFFICIENCY SAVINGS
-98%
Measured via context pruning
HEAD-TO-HEAD WIN RATE
86%
Arena paired matches
Monthly Documentation & Site Visits
440k/mo
Measured via Traffic Research bypass engine (0.5x category median)
Google Search Keyword Demand
76,000/mo
+112% YoY expansion

6-Month Web Traffic Velocity

Traffic momentum vs Category Median (850k visits/mo benchmark).

AutoGPTQ Median
850k 460k 70k MayJunJulAugSepOct May · 70.4k vs 680.0k med This Skill: 70.4k -609.6k vs Median Jun · 144.3k vs 714.0k med This Skill: 144.3k -569.7k vs Median Jul · 218.2k vs 748.0k med This Skill: 218.2k -529.8k vs Median Aug · 292.2k vs 782.0k med This Skill: 292.2k -489.8k vs Median Sep · 366.1k vs 816.0k med This Skill: 366.1k -449.9k vs Median Oct · 440.0k vs 850.0k med This Skill: 440.0k -410.0k vs Median

This repository commands strong developer search intent across Perplexity, Google AI Overviews, and Claude. High keyword demand directly correlates with active team onboarding and production dependency adoption.

Arena ELO Rating Stability

Head-to-head empirical ratings evaluated across standardized agent workflows.

1298
±18 CI
1k 1k 1k MayJunJulAugSepOct May · 1.3k vs 1.2k med This Skill: 1.3k +81 vs Median Jun · 1.3k vs 1.2k med This Skill: 1.3k +87 vs Median Jul · 1.3k vs 1.2k med This Skill: 1.3k +101 vs Median Aug · 1.3k vs 1.2k med This Skill: 1.3k +112 vs Median Sep · 1.3k vs 1.2k med This Skill: 1.3k +111 vs Median Oct · 1.3k vs 1.2k med This Skill: 1.3k +100 vs Median
WIN RATE
86%
Head-to-head
WEEKLY SURGE
+11.2%
Adoption velocity
P95 LATENCY
11ms
Execution speed
TOKEN OVERHEAD
-98%
Context saved

Head-to-Head Comparison — AutoGPTQ vs 300 Skills

Select any repository from the 300-skill benchmark graph to evaluate speed, memory, and adoption differences side-by-side.

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Popular Direct Comparisons in Category:
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