WoolyAI Acceleration Service
Hi HN, We’re a small team of OS, virtualization, and ML engineers, and after three years of development, we’re thrilled to launch the beta of our CUDA abstraction layer! We decouple the Kernel Shader execution from applications that use CUDA into a Wooly Abstraction layer. In this abstraction layer, we compile these to a new binary, and Shaders are compiled into a Wooly Instruction Set. At runtime, Kernel Shader launch events initiate a transfer of Shader over the network from a CPU host to a GPU host, where they are recompiled. Their execution is managed by Wooly Server software to achieve…
WoolyAI Acceleration Service launched on March 9, 2025 with 100 votes, #429 of 7,215 launches that month and more than 90% of that year's launches. The site was still online in September 2026, 1 year on.
What it does
WoolyAI is a CUDA Abstraction layer. On top of this layer, we have built a GPU Cloud service(WoolyAI Acceleration Service) with "Actual GPU Resources Used" billing, NOT "GPU Time Used" billing for Data Scientist to run Pytorch applications from CPU environment
Before the model acts, the signal is already there. Coming soon.
In the maker’s words, at launch
WoolyAI disaggregates GPU compute from CPU and routes GPU operations from ML jobs running on CPU-only infra into a shared heterogeneous GPU pool(Nvidia+AMD), where a GPU hypervisor packs and schedules multiple jobs per GPU for maximum utilization. Trial is now to triple your GPU Utilization in ML Development.
Does a similar job
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