about.subtitle
Working with neural network models doesn't end at the training stage. When an algorithm moves into production, performance, memory consumption, and latency take center stage. In this collection, we explore methods and approaches that allow for reduced computational costs while maintaining prediction accuracy. Here, we've gathered materials on quantization, pruning, knowledge distillation, and architecture adaptation for specific hardware solutions – ranging from mobile processors to high-load server clusters.
OpenAI has released GPT-5.3 Instant – a lightweight version of the model designed for speed and convenience in casual conversations and routine tasks.
AI: Events
Infrastructure
The new NVIDIA GB300 NVL72 server, paired with the SGLang framework, has demonstrated a 25x performance boost when running language models.
Qualcomm has introduced a comprehensive infrastructure for running large AI models, featuring a server rack, expansion cards, and a management system as a single integrated solution.
AI: Events
Technical context • Infrastructure
An exploration of how TunableOp technology enables the pre-selection of optimal parameters for neural networks, and why this is a valuable practice.
AI: Events
Technical context • Infrastructure
Alibaba Cloud has introduced a precise request routing mechanism for language models that significantly boosts caching efficiency in distributed inference.
AI: Events
Infrastructure
We explore how companies update AI services without the risk of widespread outages, and why the canary release approach is becoming an industry standard.
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