Accelerating SD Turbo and SDXL Turbo Inference with ONNX Runtime and Olive

🧨 Accelerating Stable Diffusion XL Inference with JAX on Cloud TPU v5e

Exploring simple optimizations for SDXL

FusionML: Prefill, Not Decode - Mechanism and Boundaries of CPU+GPU Co-Execution on Unified-Memory Apple Silicon

arXiv:2607. 22785v1 Announce Type: cross Abstract: Apple-Silicon SoCs share CPU, GPU, and Neural Engine over one unified memory system, raising the question of whether transformer inference can be accelerated by splitting single operators across units.

Pushing the Envelope of LLM Inference with Ultra-Low-Bit Quantized Models

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.

It\^o maps for any-step SDEs

arXiv:2606. 11156v1 Announce Type: cross Abstract: Recent one-step generative models accelerate sampling by learning deterministic flow maps of the underlying dynamics.

RoofLang: Enabling AI-Driven Architecting of LLM Inference Systems

arXiv:2609.12551v2 Announce Type: replace-cross Abstract: AI is beginning to make substantive contributions to LLM inference optimization. Existing AI optimizations are predominantly profiling-based....