AI Briefing#inference
The latest AI and developer news about #inference, with the original source and a short summary.
The latest AI and developer news about #inference, with the original source and a short summary.
It presents a methodology for optimizing hardware-specific costs and latency for large-scale classification and embedding workloads.
Hugging Face has directly integrated major serverless inference providers into the Hub and SDK.
One-click model deployment via FriendliAI is now available on the Hugging Face Hub.
FuriosaAI is joining UEC to build open standards for multi-chip LLM inference.
FuriosaAI has unveiled a **TCP** architecture that overcomes the rigidity of existing Systolic Arrays and supports flexible computation.
Hugging Face has launched HUGS, an inference service that makes it easy to deploy open models on your own infrastructure.
FuriosaAI has officially begun customer sampling of RNGD, its next-generation chip for data center inference.
FuriosaAI adopted HBM3 in its next-generation AI chip RNGD to maximize inference performance for LLM and multimodal models.
FuriosaAI unveiled RNGD, a new AI accelerator optimized for LLM and multimodal model inference, at Hot Chips 2024.
Hugging Face's TGI has launched a feature that dynamically serves multiple LoRA adapters from a single base model.
FuriosaAI has unveiled **RNGD**, a highly efficient AI accelerator optimized for LLM and multimodal model inference.
Hugging Face has integrated AMD Instinct MI300 GPUs into its platform to support model deployment and inference optimization.
Global enterprises are planning to expand AI infrastructure in 2024, with a particular focus on securing cost-efficient inference infrastructure to replace GPUs.
This introduces how to use a text generation pipeline based on Optimum Habana to efficiently run Llama 2 models on Intel Gaudi 2 accelerators.
Llama-2-70B is cheap for prompt processing, but it's at a disadvantage compared to GPT-3.5 for completion-heavy tasks.
It explains how to leverage PyTorch's meta device to overcome memory constraints and efficiently load large language models.
It explains how to optimize BERT model inference performance by leveraging the hardware and software features of Intel Xeon CPUs.
This explains optimization strategies and benchmarking methodology for maximizing BERT model inference performance in modern CPU environments.