arXiv:2607. 00501v1 Announce Type: cross Abstract: We present BaseRT, a native Metal inference runtime for large language models (LLMs) on Apple Silicon, and report the highest inference throughput on this hardware to date.
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arXiv:2607. 19438v1 Announce Type: cross Abstract: Apple's M5 generation introduces a redesigned GPU architecture in which every core carries a dedicated Neural Accelerator: on-die matrix units exposed through the Metal~4 tensor API.
arXiv:2606. 11357v1 Announce Type: cross Abstract: With the growing demand for on-device LLM inference, edge SoCs increasingly integrate NPUs to improve performance and energy efficiency under tight power and thermal budgets.
arXiv:2608.28667v1 Announce Type: cross Abstract: The rapid proliferation of Large Language Models (LLMs) has raised concerns about their environmental impact during inference. While Green AI researc...
The paper reports the development of 2‑bit microkernels for CPUs and mixed‑precision 2‑bit kernels for Intel Xe2 GPUs, achieving near‑roofline performance. Integrated into LLM inference pipelines, these kernels deliver up to 7× speedup over 16‑bit inference on CPUs and 6.7× on GPUs, surpassing the current state‑of‑the‑art bitnet.cpp runtime by 2.2×. The work demonstrates that ultra‑low‑bit LLM models can be deployed efficiently, offering significant gains in latency, memory, throughput, and energy consumption.
arXiv:2607. 14618v1 Announce Type: new Abstract: CPUs are the most universal target for on-device LLM inference, but existing low-bit quantization methods offer either coarse operating points or fine-grained mixed precision that is difficult to execute efficiently on CPUs.
arXiv:2606. 21428v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) language models are often described as ideal for resource-constrained inference.