AI Briefing#inference-acceleration
The latest AI and developer news about #inference-acceleration, with the original source and a short summary.
The latest AI and developer news about #inference-acceleration, with the original source and a short summary.
CalibAtt is a technique that speeds up video generation models by up to 1.58x by identifying unnecessary connections during attention computation.
MrFlow has been proposed as a technology that dramatically speeds up the inference of Diffusion models through step-wise resolution adjustment without additional training.
DSpark unveiled a speculative decoding framework that combines semi-autoregressive generation with confidence scheduling to solve the sharp acceptance rate decline problem of parallel drafters.
Covers how to dramatically boost inference speed for coding agents by leveraging Speculative Decoding and kernel optimization tailored to their task characteristics.
Intel Gaudi 2 accelerators significantly improved the inference and fine-tuning performance of the protein language model ProtST compared to A100.
Using 🤗 Optimum Intel, SetFit model inference speed can be accelerated up to 7.8x on Intel Xeon CPUs.
This covers how to optimize the inference performance of embedding models on Intel CPU environments using Optimum Intel and fastRAG.
Through Optimum Intel and quantization techniques, StarCoder's inference performance on Intel Xeon processors was improved by more than 7x.
Inference performance of SD Turbo and SDXL Turbo models was significantly improved through ONNX Runtime and Microsoft Olive.
Hugging Face Diffusers now supports SDXL inference on Cloud TPU v5e via JAX, improving performance and cost efficiency.
This covers how to use SmoothQuant technology to reduce LLM size and improve inference efficiency on Intel CPUs.
This introduces a method for speeding up Stable Diffusion inference by more than 2x on Intel Sapphire Rapids CPUs using OpenVINO.
It covers how to optimize inference performance of PyTorch Transformer models using Intel Sapphire Rapids CPUs and the Optimum Intel library.
Intel OpenVINO has been integrated into Hugging Face's Optimum Intel, making it easier to run model inference and quantization on Intel processors.
Hugging Face has updated the features of the Optimum library, which optimizes inference performance for Transformers models.