A developer explains the technical journey from pressing Enter to receiving a response from a large language model (LLM) like ChatGPT, Gemini, or Claude. The process involves tokenization, embedding, …
S&P Global Ratings revised its outlook on TSMC's AA- long-term issuer credit rating to Positive on June 23, citing stronger leadership in advanced high-performance computing chips. S&P estimated AI pr…
Moondream HQ reveals that GPUs often sit idle during AI model inference due to CPU overhead, a phenomenon called the 'GPU bubble.' The company's Photon system uses pipelined decoding to overlap CPU an…
MacBook GPU memory allocation depends on total system memory: up to 36GB allows 66% GPU usage, while 36GB or more allows 75%. Users can increase allocation via Terminal for AI models. Shared memory el…
Extropic unveiled thermodynamic computing hardware and algorithms that run generative AI workloads using radically less energy than GPUs. The company released its `thrml` library and plans to build a …
JAX's default_device context manager places arrays on the specified device but does not commit them, allowing JAX to move them to other devices. This caused array lookups to take over a second by trig…
Agentic AI workloads are shifting the compute ratio, making CPUs the critical coordination substrate rather than a support component for GPUs. This inversion is exposed by current Xeon supply tightnes…
Researchers introduced llada.cpp, the first NPU-aware inference framework for accelerating diffusion large language models on smartphones, achieving 17x-42x latency reduction over CPU baselines while …
Ollama, an open-source runtime for large language models, enables users to run models locally on Mac, Windows, or Linux with a single command, eliminating the need for cloud dependencies or complex en…
Shepherd Model Gateway (SMG) has disaggregated all CPU-bound workloads from GPU inference in large language model serving, moving tokenization, detokenization, and parsing into a dedicated Rust gatewa…