Train-to-Test scaling explained: How to optimize your end-to-end AI compute budget for inference

AI reasoning does not necessarily require spending huge amounts on frontier models. Instead, smaller models can yield ...
dbta

Predibase Inference Engine Offers a Cost Effective, Scalable Serving Stack for Specialized AI Models

Predibase, the developer platform for productionizing open source AI, is debuting the Predibase Inference Engine, a comprehensive solution for deploying fine-tuned small language models (SLMs) quickly ...
Business Wire

Predibase Launches Next-Gen Inference Stack for Faster, Cost-Effective Small Language Model Serving

Predibase's Inference Engine Harnesses LoRAX, Turbo LoRA, and Autoscaling GPUs to 3-4x Throughput and Cut Costs by Over 50% While Ensuring Reliability for High Volume Enterprise Workloads. SAN ...
Hosted on MSN

MetalRT brings the first unified AI inference engine to Apple Silicon

Artificial intelligence is rapidly moving beyond cloud servers and into the devices people use every day. Laptops, smartphones and edge systems now have enough computing power to run sophisticated ...
EDN

NTT develops AI inference chip for 4K video processing

At its Upgrade 2025 annual research and innovation summit, NTT Corporation (NTT) unveiled an AI inference large-scale integration (LSI) for the real-time processing of ultra-high-definition (UHD) ...
PC Magazine

AI training vs. inference

The simplest definition is that training is about learning something, and inference is applying what has been learned to make predictions, generate answers and create original content. However, ...
VentureBeat

The team behind continuous batching says your idle GPUs should be running inference, not sitting dark

Every GPU cluster has dead time. Training jobs finish, workloads shift and hardware sits dark while power and cooling costs keep running. For neocloud operators, those empty cycles are lost margin.