Apple, Huawei, Acer, System76 and others are investing in dedicated AI silicon and workstations for local inference, from advanced Neural Engines and NPUs to multi-GPU desktops and self-hosted servers.
The new Apple A20 Pro SoC doubles the Neural Engine from 16 to 32 cores and, thanks to a 96-bit LPDDR5X bus on a 2nm process, reaches about 115 GB/s of memory bandwidth. A costly investment that signals Apple's strategic priority for local model exec...
System76 has introduced the Thelio Mira AI, a desktop designed for multi-GPU AI training, fine-tuning, inference, computer vision, and image generation. The system pairs AMD Ryzen 9000 CPUs with NVIDIA or AMD GPUs and can also handle simulations and ...
Huawei has put LogicFolding to the test on the Kirin 9050 Pro: according to the source, the density gain reaches 55% and NPU power consumption drops by 66%. The data points to a clear direction for local inference: denser chips and more efficient NPU...
A builder spent €18,000 on an EPYC server with 768 GB of VRAM and 256 GB of RAM to run frontier open source LLMs locally. But upcoming models such as GLM 6 could be two to three times larger, making the machine unusable at 4-bit quantization. The cas...
Acer's decision to bet on RTX and GoogleBook signals a hardware shift toward local inference. On-site AI demand grows for sovereignty, latency, and cost control reasons, pushing vendors to invest in GPUs and devices for self-hosted workloads.
A new TTS stack with 294k parameters occupies 337 KB in INT8 and runs on a $3 microcontroller with 512 KB of SRAM and no NPU. It supports 11 voices and 6 languages; the 1.5M-parameter model beats larger networks on SCOREQ. A signal for local voice AI...
