Reverse-Engineering the RK3588 NPU to Run Vision Transformers 15x Faster
Reverse-engineering the Rockchip RK3588 NPU to run SmolVLM 15x faster by discovering hardware limits, defeating compiler optimizations, and building a custom sharding runtime
Read the full articleYou might also wanna read
Reverse Engineering Qualcomm's Undocumented NPU Compiler Reveals Hidden Features
I reverse engineered the Qualcomm NPU compiler to find out about an optimization solver, secret precision rewrites, and a simulator nobody m
datavorous.github.io·25d ago
A Survey on the Green Development of Large Models: From Resource-Efficient Architectures to Hardware-Software Co-Design
arXiv:2607.09084v1 Announce Type: new Abstract: The rapid expansion of large-scale AI models has led to significant performance breakthrough
One GPU, Two Bottlenecks: Serving Vision-Language Models Without Wasting Silicon
You shipped a vision-language model on the same GPU stack that served your text LLM. Same vLLM config, similar parameter count, no red alarm
The Inference Alpha: Maximizing Frontier Models on AMD
At DigitalOcean, we’re committed to providing high-performance infrastructure for the next generation of AI, which is why we’ve been focused
Bonsai-27B: A 27B-Parameter Binary Transformer Model for Efficient Local AI Inference
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
EXO Labs Runs Llama 2 AI Model on 1997 Pentium II Using BitNet Optimization
EXO Labs ran Llama 2 on a 1997 Pentium II using BitNet, showing AI efficiency can outpace hardware limits.
news.bitcoin.com·1mo ago
Comments
Sign in to join the conversation.
No comments yet. Be the first.