arXiv (All)
open access
GPU Goldmines: Tunable WebGPU Kernels for Edge LLM Inference
As demand for LLM inference grows and consumer GPUs grow more capable, edge deployment is becoming increasingly attractive. WebGPU is compelling in this setting because it offers cross-platform portability and browser accessibility. However, these same strengths also make high performance difficult
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- GPU Goldmines: Tunable WebGPU Kernels for Edge LLM Inference#1318973
- A Rapid Pipeline for Training and Deploying ML Models on WeBe Band#1077013
- AMDKernelVault: Large-Scale Datasets and Agentic Training for AMD GPU Kernel Optimization#1069969
- Hardware Acceleration of Block-Diffusion LLM for Edge Devices#821064
- Xronos: Heterogeneity-Aware Tensor Parallelism for Collaborative LLM Fine-Tuning on Edge CPUs#1014955
Record · ID 1342197
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive