Llamas on the Web: Memory-Efficient, Performance-Portable, and Multi-Precision LLM Inference with WebGPU
arXiv:2605.20706v1 Announce Type: cross Abstract: Running language models in the browser presents a unique opportunity to build efficient, private, and portable AI applications, but requires contending with constrained memory availability and heterogeneous hardware targets. To realize this opportunity, we present Llamas on the Web (LlamaWeb), a WebGPU backend for llama.cpp that enables memory-efficient and performance-portable LLM inference across a wide range of model weight formats in the browser. Our design significantly reduces memory overhead through static memory planning and efficient m
The proliferation of open-source LLMs and advancements in web technologies like WebGPU are converging, making efficient browser-based AI inference a critical development path.
This development enables more private, accessible, and potentially offline AI applications, diversifying the compute landscape for AI inference away from centralized cloud providers.
Local browser-based LLM inference becomes significantly more viable and performant, reducing reliance on remote servers and potentially fostering new application paradigms.
- · WebGPU developers
- · On-device AI application developers
- · Edge computing providers
- · Users prioritizing data privacy
- · Cloud-centric LLM API providers
- · Developers solely focused on server-side inference
- · High-latency AI applications
Widespread adoption of client-side LLMs will reduce server load and improve user experience for many AI applications.
New privacy-preserving AI products and services will emerge, leveraging the ability to run sensitive data inference locally.
The reduced barrier to deploying AI could accelerate the development of personalized, agentic AI assistants directly integrated into browsers or operating systems.
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Read at arXiv cs.LG