Sharing actual GPU core and VRAM utilization metrics for query on 10 LLM models https://lnkd.in/g6Qtj9X5?
关于我们
We have built an abstraction layer for CUDA, which enables applications that use CUDA(Pytorch and others) to run on non-GPU environments and use a remote WoolyAI GPU Acceleration Service. Data Scientists and Machine Learning developers who work with Pytorch can run their dev/test inside non-GPU Linux container environments, which will execute all CUDA calls on a remote GPU Acceleration Service. Additionally, the CUDA abstraction enables us to measure the exact GPU core and memory resource used by each execution and charge back the user for actual utilization and not total GPU execution time used.
- 网站
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https://www.woolyai.com
WoolyAI的外部链接
- 所属行业
- 桌面计算软件产品
- 规模
- 2-10 人
- 类型
- 私人持股
- 创立
- 2024
- 领域
- Machine Learning Infrastructure、GPU Infrastructure Management和GPU Cloud
WoolyAI员工
动态
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Running Pytorch apps inside non-GPU environments with CUDA - WoolyAI Getting Started Video https://lnkd.in/eWJtjpRU
WoolyAI - Brief Intro and Getting started
https://www.youtube.com/
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We are doing a Beta - Running Pytorch environments inside non-GPU containers on your laptop or your Cloud instances with remote GPU Acceleration Service This newly launched interesting technology allows users to run their Pytorch environments inside CPU-only containers in their infra (cloud instances or laptops) and execute GPU acceleration through remote Wooly AI Acceleration Service. Also, the usage is based on GPU core and memory utilization and not GPU time Used. https://lnkd.in/eHTFPB6s