#vllm

China Telecom’s new Xing4.0‑29B‑A4B MoE LLM delivers 256K‑token context, 4B active params, and 96% faster Ascend‑NPU training while outperforming rivals on key benchmarks.

LM Studio users fume over new site layout that pushes Bionic Agent over their familiar tool, hiding downloads and complicating model compatibility.

Open‑source Jenny lets you run LLMs locally on Windows—no cloud calls, safe rollbacks, full IDE, and multi‑model support—all free under MIT.

Open‑weight AI models face benchmark gaps and speculative decoding trade‑offs—discover how scale and MTP settings drive real‑world performance.

llama.cpp now supports the efficient Spark‑X2.5 models with a million‑token window, while an unverified MoE experiment promises faster expert‑expansion.

Rapid AI model releases hit new heights, but hardware limits still block deployment. Learn how GPUs, vLLM, and mining rigs stack up and what legal battles mean for the future.

Reddit users warn high failure rates for the CMP 170HX GPU: two of five cards failed in two weeks, with defects like dead tensor cores and CUDA errors.

Explore Qwen 3.8 27B’s breakthrough inference speed, custom kernels, and low‑VRAM quantization for consumer GPUs—real‑world benchmarks show 2,000 t/s prefill, 132 t/s decode, and 12‑GB models running on a 4070 ti.