Beyond advanced mathematics or theoretical computing breakthroughs, PQC is about protecting the systems enterprises already ...

Goodhart's Law ("When a measure becomes a target, it ceases to be a good measure.") has been around long enough that it ...

Vienna startup Ora Computing raised €3.5M and proved a 70-billion-parameter large language model can be compressed for under ...

Companies running large language models face a persistent bottleneck: the memory consumed by key-value caches during inference grows with every token generated, forcing operators to choose between ...

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors ...

Neural network quantization is an established technique for compressing real-valued models, but its application to complex-valued networks—essential in electromagnetics, acoustics, and quantum physics ...

A month ago, I thought int4 quantization was the hard part. I was wrong. Quantizing Qwen3-TTS broke the model (and me) in subtle and dramatic ways. Infinite loops. Stopping failures. Precision ...

Abstract: Communication is widely known as the primary bottleneck of federated learning, and quantization of local model updates before uploading to the parameter server is an effective solution to ...

Experts At The Table: AI/ML is driving a steep ramp in neural processing unit (NPU) design activity for everything from data centers to edge devices such as PCs and smartphones. Semiconductor ...

If VRAM is the brake pedal on local LLMs, quantization is how we ease the pressure. At its core, it’s simple: store numbers with fewer bits. But in practice, modern methods like GPTQ, AWQ, and GGUF ...