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Colossal-AI

Data confidence: High

Distributed training framework for large-scale AI models

DMI: 16#76 of 262 (Top 30%)HN 30d: 0 41,362 · 4,527 forks
AI InfrastructureMethodologyVisit Website →

Colossal-AI has a Developer Momentum Index of 16, ranking #76 of 262 companies. Key metrics include 12.5K monthly package downloads, 41.4K GitHub stars, $28M in total funding.

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Developer Adoption

Developer Adoption

Package downloads and ecosystem metrics, 30-day window

PyPI Downloads (30d)
12.5K
colossalai

PyPI Download Trend

11,42211,73412,04612,35712,669Mar 5Mar 6
90-day trend↑ +7.3%

PyPI Downloads grew from 11,624 to 12,467 (+7.3%) over the last 90 days.

View data table
Datepypi downloads
Mar 5, 202611,624
Mar 6, 202612,467
Key Signals

Developer Community Metrics

Metrics computed from HN discussion, GitHub activity, and funding data.

HN vs Category0.0x category median (0 vs 2 median)
Funding Trajectory2 rounds in 8 months, ($6M → $22M)

Category Benchmark

75 AI Infrastructure companies
12.5K downloads/mo<1% of median
0Median: 1.3M2.5M+

Funding Rounds

Series A$22M

2023 · Sinovation Ventures, ZhenFund

Series A$6M

2022 · BlueRun Ventures

SeedUndisclosed

2021

Cite This Data

According to AI-Buzz, Colossal-AI has 12,467 PyPI downloads in 30 days.

Source: https://www.ai-buzz.com/companies/colossalai?utm_source=citation&utm_medium=referral&utm_campaign=cite_this_data

Metrics derived from public APIs (HN Algolia, GitHub, npm/PyPI). Sentiment classified by AI. See methodology for details →

About

About

Description

Distributed training framework for large-scale AI models

Estimated Company Size

11 - 50 employees

Details

Founded

2021

Data Confidence

Excellent
91/ 100

Reflects profile completeness, metric freshness, and signal coverage.

How we calculate this

Open Source

41.4K
Stars
4.5K
Forks
ActivePython
Last commit: 5 days ago
View on GitHub →

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Weekly AI Intelligence

Which AI companies are developers actually adopting? We track npm and PyPI downloads for 262+ companies. Get the biggest shifts weekly - before they show up in the news.