LlamaWeb is a WebGPU backend for llama.cpp that uses static memory planning, tunable kernels, and templated multi-precision support to cut memory use by 29-33% and raise decode throughput by 45-69% versus prior browser frameworks on tested hardware.
Available: https://arxiv.org/abs/2511.07885
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3representative citing papers
Quantitative benchmarks across recent AI accelerators reveal that optimal hardware choice varies with workload parameters and that several platforms incur substantially higher idle power than GPUs.
AgentStop uses execution signals to early-terminate failing local LLM agent trajectories, cutting energy use 15-20% with minimal utility loss.
citing papers explorer
-
The xPU-athalon: Quantifying the Competition of AI Acceleration
Quantitative benchmarks across recent AI accelerators reveal that optimal hardware choice varies with workload parameters and that several platforms incur substantially higher idle power than GPUs.