yes including cudnn which is now packaged on pypi
OPEN SOURCE
-
PyTorch Remains Available on Conda via Conda-Forge
By
–
to be clear pytorch is available on conda via conda-forge and will continue to be.
-
PyTorch CUDA Dependencies Available via pip packages now
By
–
@_seemethere can answer a few of your questions, especially around analysis of usage.
pytorch pip packages don't need the cudatoolkit, and we've been working with NVIDIA (thanks to @ptrblck_de
) to have cublas, cudnn etc. available via pip as well. -
01.ai Trains #6 World Model for $3M with $0.14/M Token Inference
By
–
01.ai trained the #6 model in the world for $3M pre-train cost. And the inference price is $0.14/million tokens! tomshardware.com/tech-indust…
-

Yi Models Integration with CAMEL Framework for Multi-Agent AI
By
–

🎉Love seeing Yi models in @CamelAIOrg! Powerful Yi models join forces with this awesome multi-agent framework. Can't wait to see what AI agents you'll build! #YiLightning #LLM #AI CAMEL-AI.org (@CamelAIOrg) 📢 We've just added support for the Yi-series of LLM models in the 🐫 CAMEL framework! This enhancement allows users to leverage various performance tiers with models like yi-lightning, yi-large, yi-medium, and yi-large-turbo, providing greater flexibility in language processing tasks. Thanks to our contributor MuggleJinx for this significant contribution! 🤝 Explore more here: github.com/camel-ai/camel/pu…. — https://nitter.net/CamelAIOrg/status/1857118366730776719#m
-

01.ai Trains GPT-4 Competitor with 95% Fewer Resources
By
–
Chinese startup 01 .ai trains competitive LLM using 95% fewer resources through innovative engineering optimization. 01 .ai trained a GPT-4 competitor using just 2,000 GPUs and $3M, while achieving competitive performance. Through innovative engineering and optimization techniques, they achieved what OpenAI did with $80-100M, demonstrating remarkable cost efficiency in LLM training. → Training Resource Optimization at 01 .ai Using only 2,000 GPUs versus OpenAI's estimated 10,000+ GPUs for GPT-3. The company achieved competitive performance despite severe hardware constraints due to US regulations. → Cost Efficiency Breakthrough $3M total training cost compared to OpenAI's $80-100M for GPT-4. Model ranked sixth in performance according to UC Berkeley's LMSIS benchmark. → Technical Innovation in Inference Transformed computational problems into memory-oriented tasks. Built multi-layer caching system and specialized inference engine. Achieved inference costs of 10 cents per million tokens – 1/30th of industry standard. → Engineering Focus Areas Prioritized GPU resource allocation. Optimized both training speed and inference efficiency. Developed custom inference architecture for maximum hardware utilization.
-

GPT-4o Mini vs Llama 3.1: Key Comparison
By
–
GPT-4o Mini vs Llama 3.1. Choose AI to fit your needs. • Assess key features
• Check performance metrics
• Determine ideal use cases Read more: https://
buff.ly/3O7jdhR -

Open Source AI: Benefits and Challenges at Slush 2026
By
–
Our Chief Technologist & Co-Founder, @KunleOlukotun will be onstage at @SlushHQ on Nov. 20th. He’ll be speaking about the benefits & challenges of #opensource #AI alongside: @Thom_Wolf – @huggingface @graceisford – @Lux_Capital @ravmattu – @nytimes
-

Cerebras Partner Launches TxT360 Trillion Token Dataset
By
–
Cerebras’ partner @mbzuai has announced TxT360 (Trillion eXtracted Text) — the first globally deduplicated dataset across most used data sources for LLM pretraining, and an optimized upsampling recipe to expand to 15T+ tokens of high-quality open-source data for pretraining LLMs.
-

Open Source and Science: The Path to Success
By
–
if you're not opting open source/ science – you're ngmi!