AI Briefing#onnx-runtime
The latest AI and developer news about #onnx-runtime, with the original source and a short summary.
The latest AI and developer news about #onnx-runtime, with the original source and a short summary.
Hugging Face has released 207 optimized WebGPU kernels and a loader library, improving browser-based AI inference performance by an average of 2.57x.
Manticore Search has introduced an ONNX Runtime backend, boosting embedding inference speed by an average of 14x compared to before.
A C++ port of Echo-TTS that supports efficient inference using GGML and ONNX Runtime has been released.
On-device streaming ASR achieved **8.20% WER** and **0.56s** latency on CPU.
Transformers.js v4 introduces a WebGPU backend, enabling high-performance AI model execution in both browser and server environments.
Transformers.js v4, featuring the introduction of a WebGPU runtime and performance optimizations, has been released via NPM.
Inference performance of SD Turbo and SDXL Turbo models was significantly improved through ONNX Runtime and Microsoft Olive.
ONNX Runtime supports over 130,000 models on Hugging Face, accelerating inference performance.
By integrating Hugging Face's Optimum library with ONNX Runtime, training speed for transformer models has been improved by up to 130%.
Hugging Face has updated the features of the Optimum library, which optimizes inference performance for Transformers models.
Hugging Face has unveiled a technical approach that improves Transformer inference speed by 100x through library optimization and hardware-specific compilation.