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Evaluating the Applicability of LLM Inference Optimizations on Apple Silicon

Kyrylo Yemets, Andrii Yaroshevych, Victor Muryn, Ostap Khomenko, Pavlo Kryven, Maksym Kmet, Maksym Shamrai, Mariya Hirna

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Tags: Apple, Apple M2 Pro, Apple M3 Ultra, Apple M4 Pro, Benchmarking, Computer science, CUDA, LLM, nVidia, nVidia GeForce RTX 4090

October 28, 2026 by hgpu

Qwen3-235B @ 1M tokens on AMD MI355X

CHUNDER: Enabling Million-Token Qwen3 Serving on AMD ROCm

CUDA++ for the NVIDIA DGX Spark

Reverse Engineering the CUDA Software Stack: A Clean-Room GPU Machine-Code Toolchain for the NVIDIA GB10

ATTUNE: Agentic Throughput Tuning for LLM Training

ATTUNE: Agentic Throughput Tuning for LLM Training

HPCAgent-Bench: A Framework for Sound Benchmarking of Code-Optimizing Agents, Harnesses, and Skills

HPCAgent-Bench: A Framework for Sound Benchmarking of Code-Optimizing Agents, Harnesses, and Skills

Brian2 Atlas

A Unified Intermediate Representation and Execution Architecture for Heterogeneous and Distributed Neural Simulation

AgentGarten: Code Worlds for Evolving Agents

AgentGarten: Code Worlds for Evolving Agents

KernelLens: Automated Triton-to-C++ Compiler for Enterprise Inference Runtimes

KernelLens: Deploying Triton Kernels to C++ Inference Engines

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

Automatically Generating ML Compiler Backends from Tensor Accelerator ISA Descriptions

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