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APEX4: Efficient Pure W4A4 LLM Inference via Intra-SM Compute Rebalancing

 💬LLMs  Content type: Academic
arxiv.org·

If Claude Fable stops helping you, you’ll never know

 💬LLMs

LLM-Based Porting of Optimized C++ to CUDA Through Deoptimization and Reoptimization

 📐Scaling Laws  Content type: Academic
arxiv.org·

SET: Stream-Event-Triggered Scheduling for Efficient CUDA Graph Pipelines

 🔥PyTorch  Content type: Academic
arxiv.org·

AutoMegaKernel: A Statically-Checked Agent Harness for Self-Retargeting Megakernel Synthesis

 💬LLMs  Content type: Academic
arxiv.org··Hacker News

Joint Structural Pruning and Mixed-Precision Quantization for LLM Compression

 💬LLMs  Content type: Academic
arxiv.org·

Learned Subspace Compression for Communication-Efficient Pipeline Parallelism

 🧠AI Research  Content type: Academic
arxiv.org·

Enhancing AI Interpretability and Safety through Localised Architectures

 🔍Interpretability  Content type: Academic
arxiv.org·

Resource-aware Computation-Communication Overlap for multi-GPU ML Workloads

 📉Deep Learning  Content type: Academic
arxiv.org·

Demystifying NVSHMEM: A System-Level Analysis on Symmetric Memory and Device-Initiated Operations in GPU Communication

 📐Scaling Laws  Content type: Academic
arxiv.org·

Breaking the Bubble: Asynchronous Pipeline Parallel Training with Bounded Weight Inconsistency

 ⚙️Model Training  Content type: Academic
arxiv.org·

On GPU Implementation for Multi-Precision Integer Division

 📐Scaling Laws  Content type: Academic
arxiv.org·

PALUTE: Processing-In-Memory Acceleration via Lookup Table for Edge LLM Inference

 💬LLMs  Content type: Academic
arxiv.org·

Floating-point autotuning with customized precisions

 📐Scaling Laws  Content type: Academic
arxiv.org·

BIDENT: Heterogeneous Operator-level Mapping for Efficient Edge Inference

 📐Scaling Laws  Content type: Academic
arxiv.org·

Communication Strategy Selection for Multi-GPU 3D FDTD with Convolutional Perfectly Matched Boundary Layers

 📐Scaling Laws  Content type: Academic
arxiv.org·

SpectrumKV: Per-Token Mixed-Precision KV Cache Transfer for Prefill-Decode Disaggregated LLM Serving

 💬LLMs  Content type: Academic
arxiv.org·

StageFrontier: Synchronization-Aware Stage Accounting for Distributed ML Training

 📉Deep Learning  Content type: Academic
arxiv.org·

STAR-KV: Low-Rank KV Cache Compression via Soft Thresholding for Adaptive Rank Control

 📐Scaling Laws  Content type: Academic
arxiv.org·

SABLE: GPU-Based Power Flow Accelerator for Sparsity-Aware Batched Learning

 📉Deep Learning  Content type: Academic
arxiv.org·

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