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arxiv: 2605.20706 · v1 · pith:Z7TJCSCUnew · submitted 2026-05-20 · 💻 cs.DC · cs.AI· cs.LG

Llamas on the Web: Memory-Efficient, Performance-Portable, and Multi-Precision LLM Inference with WebGPU

Reese Levine , Rithik Sharma , Nikhil Jain , Abhijit Ramesh , Zheyuan Chen , Neha Abbas , James Contini , Tyler Sorensen This is my paper

Pith reviewed 2026-05-21 02:48 UTC · model grok-4.3

classification 💻 cs.DC cs.AIcs.LG
keywords WebGPULLM inferencebrowserllama.cppquantizationperformance portabilitymemory efficiency
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The pith

LlamaWeb is a WebGPU backend for llama.cpp that cuts browser LLM memory use by 29-33 percent while raising decode throughput by 45-69 percent across devices.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces LlamaWeb as a new backend for llama.cpp that runs large language models directly inside web browsers. It lowers memory demands through upfront static planning of memory allocation and streamlined model loading. A tunable set of GPU kernels handles differences across hardware and browsers while supporting several model weight formats at once. Tests across 16 devices from eight vendors and four weight formats show the claimed memory and speed gains over other browser frameworks. The work matters because it opens a path to private, device-local AI tools that do not require sending data to remote servers or installing native software.

Core claim

LlamaWeb enables memory-efficient and performance-portable LLM inference in the browser by reducing memory overhead through static memory planning and efficient model loading, addressing cross-device variability through a tunable kernel library, and supporting multiple quantization formats through templated GPU kernels.

What carries the argument

Templated GPU kernels inside a tunable kernel library that together support multiple quantization formats while adapting to different devices and browsers.

If this is right

  • LLM inference becomes feasible on a wider range of consumer hardware without custom native code.
  • Multiple quantization formats can be supported from a single kernel codebase with minimal added overhead.
  • Browser-based applications can keep model weights and computations entirely on the client device.
  • Performance remains competitive with vendor-specific llama.cpp backends on some platforms.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same static-planning approach could be applied to other inference engines to reduce browser memory footprints.
  • Local browser execution inherently keeps user prompts and outputs off remote servers, improving privacy for interactive AI tools.
  • Extending the kernel library to additional low-precision formats would further widen the set of runnable models on memory-limited devices.

Load-bearing premise

The performance and memory measurements collected on the 16 tested devices and four weight formats are representative of typical real-world browser usage patterns and hardware variability.

What would settle it

Running the same models on a device-browser pair outside the original test set and checking whether memory reduction stays inside the 29-33 percent band and decode speedup stays inside the 45-69 percent band.

Figures

Figures reproduced from arXiv: 2605.20706 by Abhijit Ramesh, James Contini, Neha Abbas, Nikhil Jain, Reese Levine, Rithik Sharma, Tyler Sorensen, Zheyuan Chen.

Figure 1
Figure 1. Figure 1: Overview of llama.cpp’s core and backend design. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Breakdown of the LlamaWeb llama.cpp WebGPU [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Memory usage for the llama model with f16 weights during inference. memory usage for the unified tab process for Safari, and the tab pro￾cess and GPU renderer process for Chrome. Each framework was prompted to generate sufficient decode output for measurement. In all four cases, LlamaWeb uses the least memory, with a geo￾metric mean of normalized peak memory usage 49% lower than WebLLM and 41% lower than T… view at source ↗
Figure 5
Figure 5. Figure 5: Throughput of the llama model across different llama.cpp backends and weight formats. The native backend is CUDA on the NVIDIA GPU, HIP on the AMD GPU, SYCL on the Intel GPU, and Metal on the Apple GPU. backend underperforms the WebGPU backend during prefill by 3×, but outperforms even the native HIP backend during decode by 38% on the q4_k_m model. On the Intel GPU, the WebGPU backend, with safety checks … view at source ↗
Figure 7
Figure 7. Figure 7: Throughput on the llama model across four weight formats (q2_k, q4_k_m, q8_0, f16), grouped by the same device clusters as in Sec. 6.1. running LlamaWeb natively with checks from Sec. 6.2, LlamaWeb outperforms the prefill numbers from other frameworks on every device except WebLLM on the Apple M4 Pro, with a geometric mean speedup of 88% over WebLLM and 205% over Transformers.js. Therefore, although subgro… view at source ↗
Figure 8
Figure 8. Figure 8: Coverage matrices for the portability and cross-quantization studies. Each cell reports throughput on a shared log [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Per-device prefill and decode throughput by model, part 1 of 2. [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Per-device prefill and decode throughput by model, part 2 of 2. [PITH_FULL_IMAGE:figures/full_fig_p018_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Per-device prefill and decode throughput for [PITH_FULL_IMAGE:figures/full_fig_p019_11.png] view at source ↗
read the original 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 model loading, addresses cross-device variability through a tunable kernel library, and introduces templated GPU kernels that support performant implementations of numerous quantization formats, enabling broad model support and extensibility to new formats. We evaluate LlamaWeb on 16 devices from 8 vendors, collecting data from 10 language models and four model weight formats. We compare LlamaWeb against existing browser-based LLM frameworks and find that LlamaWeb requires 29-33% less memory across several combinations of device, browser, and operating system. We also evaluate LlamaWeb's performance against these frameworks and find that it increases decode throughput by 45-69% across four GPUs from separate vendors. In addition, we compare LlamaWeb's performance against other llama$.$cpp backends, where it is competitive with and even beats vendor-specific backend performance on some devices.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript presents LlamaWeb, a WebGPU backend for llama.cpp enabling memory-efficient, performance-portable, and multi-precision LLM inference in browsers. It uses static memory planning and efficient model loading to reduce overhead, a tunable kernel library to handle device variability, and templated GPU kernels supporting multiple quantization formats. Evaluations on 16 devices from 8 vendors with 10 models and 4 weight formats report 29-33% lower memory usage versus other browser frameworks and 45-69% higher decode throughput on four GPUs from separate vendors, while remaining competitive with other llama.cpp backends.

Significance. If the reported gains prove robust, this implementation could meaningfully advance browser-based LLM deployment by mitigating memory constraints and hardware heterogeneity while preserving privacy. Notable strengths include the broad multi-vendor device coverage, explicit support for multiple weight formats with extensibility, and direct integration with the established llama.cpp ecosystem, which facilitates reproducibility and adoption.

major comments (1)
  1. [§5] §5 (Evaluation): The headline claims of 29-33% memory reduction and 45-69% throughput improvement rest on measurements from 16 devices. The section provides no device-selection criteria, run-to-run variance or error bars, and no sensitivity analysis for WebGPU driver differences, shader compilation overhead, or sandbox memory behavior under concurrent tab load. These omissions are load-bearing for assessing whether the quoted percentages generalize beyond the tested sample.
minor comments (2)
  1. [Abstract] Abstract: The comparison baselines for the memory and throughput numbers are referenced only generically; naming the specific competing browser frameworks and llama.cpp backends in the abstract would improve immediate clarity.
  2. [§4] §4 (Implementation): The description of the templated kernel library would benefit from a small table listing supported quantization formats and their corresponding kernel variants for quick reference.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback, which highlights important considerations for strengthening the evaluation section. We address the major comment point by point below and outline the revisions we will make.

read point-by-point responses

  1. Referee: [§5] §5 (Evaluation): The headline claims of 29-33% memory reduction and 45-69% throughput improvement rest on measurements from 16 devices. The section provides no device-selection criteria, run-to-run variance or error bars, and no sensitivity analysis for WebGPU driver differences, shader compilation overhead, or sandbox memory behavior under concurrent tab load. These omissions are load-bearing for assessing whether the quoted percentages generalize beyond the tested sample.

    Authors: We agree that additional methodological details are needed to support the generalizability of the reported gains. In the revised manuscript, we will expand §5 with explicit device-selection criteria, explaining that the 16 devices were chosen to span 8 vendors and a range of performance tiers (from integrated graphics to high-end discrete GPUs) to evaluate portability. We will also add run-to-run variance information and error bars for the memory and throughput metrics, based on repeated measurements collected during our experiments. For sensitivity to WebGPU driver differences, shader compilation overhead, and concurrent-tab memory behavior, we will include a new discussion subsection acknowledging these factors as sources of variability in browser environments and reporting any qualitative observations from our testing across browsers and OSes. These revisions will be made without altering the core results. revision: yes

Circularity Check

0 steps flagged

No significant circularity; claims rest on direct implementation and empirical benchmarks

full rationale

The paper describes an engineering implementation of a WebGPU backend for llama.cpp, including static memory planning, tunable kernels, and templated quantization support. All headline performance numbers (29-33% memory reduction, 45-69% decode throughput gains) are presented as direct outcomes of measurements collected on 16 devices across 8 vendors, 10 models, and 4 weight formats. No equations, fitted parameters, or uniqueness theorems are invoked; the central claims do not reduce to self-citations or to quantities defined in terms of the reported results. The work is therefore self-contained as an implementation-plus-measurement contribution with external comparisons.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

This is an applied systems paper whose central claims rest on implementation choices and empirical measurements rather than mathematical axioms or new physical entities.

axioms (1)
  • domain assumption WebGPU is available and sufficiently stable on the target browsers and devices for the reported kernels to execute.
    The entire system depends on the WebGPU API existing and behaving as expected across the tested hardware.

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