Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.11932

Throughput per Megabyte: A Pilot Benchmark of Language-Stack Efficiency for Self-Hosted HTTP Services on a Raspberry Pi 5

William Oliveira
View original source ↗Read source document ↗Export metadata ↓

Abstract

Cloud-centric web benchmarks miss constraints that matter for self-hosted services on ARM64 single-board computers, especially idle RAM footprint and energy per request. We ran a pilot benchmark on one Raspberry Pi 5, measuring equivalent SQLite-backed CRUD APIs implemented in Go 1.26/net/http, Rust 1.95/Axum, Python 3.13/FastAPI+Granian, Node.js 24/Fastify, and .NET 10 Native AOT across N=50 randomized runs per stack and endpoint, with a separate concurrency sweep and memory time-series collection. Rust achieved the highest weighted throughput-to-RAM ratio (310.25 req/s/MB, 2.8x Go) because its weighted peak RSS was 7.36 MB, while .NET, Node.js, Go, and Rust formed a raw-throughput cluster with overlapping bootstrap CIs (2,125--2,461 req/s) and Python remained below that cluster at 969 req/s. The concurrency sweep on GET /items/:id showed divergent scaling: .NET reached 26,209 req/s at c=240, Go and Rust plateaued near c=120, Node.js saturated near 9,700 req/s, and Python stayed below 2,300 req/s; memory snapshots showed Rust holding a flat 7.8 MB RSS while Node.js grew by 117 MB without visible GC drops. For RAM-constrained self-hosted CRUD-over-SQLite deployments on this Pi 5 unit, the data support Rust as the most resource-efficient measured stack, .NET as the strongest high-concurrency stack, and Go as a practical option where ecosystem breadth matters; replication is needed before extending these findings to other devices or storage backends.

Explore related subjects

cs.PFcs.DCcs.PL

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

William Oliveira. 2026-05-23. Throughput per Megabyte: A Pilot Benchmark of Language-Stack Efficiency for Self-Hosted HTTP Services on a Raspberry Pi 5. https://arxiv.org/abs/2609.11932

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

SARA: SLO-Aware Resource Allocation for Disaggregated Agentic LLM Services

Recent advances in large language models (LLMs) are driving the emergence of multi-modal and agentic services for mobile users through cloud and edge infrastructures, where long-context workloads pose daunting challenges for inference latency. Existing disaggregated LLM serving systems largely rely on hardware profiling, configuration enumeration, or heuristic scheduling, offering limited analytical guidance for cost-efficient resource allocation. In this paper, we propose SARA, a Service level objectives (SLOs)-Aware Resource Allocation framework for disaggregated agentic LLM serving systems, which maximizes goodput under a deployment cost constraint and a series of quantile-based SLO constraints. By capitalizing on queuing theory, we first model the prefill, KV cache transfer, and decode stages as an M/G/k queue, an M/G/1 queue, and a generalized birth-death process, respectively. The analysis reveals that the prefill and decode stages are dominantly limited by computational capacity and high-bandwidth memory (HBM) resources, respectively. With these mathematical models, we further derive tractable tail behaviors of different stage-wise service level metrics for both light- and heavy-tailed workloads. These characterizations explicitly map workload, model architecture, and hardware parameters to stage-wise SLO constraints and minimum resource requirements. Finally, we develop an effective resource allocation framework to maximize system goodput under limited cost budgets. Simulation and hardware results demonstrate that the proposed framework accurately predicts the stage-wise SLO with mean errors below 5%, and improves system goodput by 26.6% on average over state-of-the-art baseline methods under the same deployment cost.

cs.PF↗
arXiv2026

Performance Analysis of Low-Order, GPU-accelerated Finite Element Kernels using Kokkos

We study performance portability for low-order, matrix-free finite element kernels, using the example of a vectorial, variable-coefficient PDE operator originating in geophysical models. Written in Kokkos, the kernel is compared on NVIDIA H100, AMD MI250X, AMD MI300A and Intel PVC Max 1550 GPUs. Owing to its low order and to optimizations that reduce the arithmetic, the kernel has a low arithmetic intensity, so that its performance is determined by how the finite element assembly is mapped onto the memory hierarchy. This is a dimension in which the architectures differ even within one vendor family, causing different performance characteristics. We examine how Kokkos' hierarchical parallelism and shared scratch memory, which are used for the shared degrees of freedom of the conforming discretization, behave on each device. Finally, we show how portability gaps can be narrowed with tuning levers such as the size of the thread groups, the balance between occupancy and register use, and the atomic accumulation strategy at the end of the kernel.

cs.PF↗
arXiv2026

Measured Joules, Learned Routes: Learning to Route for Energy-Efficient LLM Serving

Large language models (LLMs) and agentic AI systems are creating rapidly growing inference energy demands as model sizes grow and reasoning trajectories extend. While in practice, many queries do not require the capabilities of the largest available model, and routinely directing such queries to a high-capability model can introduce unnecessary, considerable computation and energy consumption. In this paper, we investigate whether adaptive routing across a heterogeneous pool of LLMs can reduce this energy burden without substantially compromising task performance. We design a language-model-based router that reads in each query and selects an answer model from a fixed candidate pool. The candidate models are first profiled through an offline tournament that records their correctness, latency, power, and GPU energy for each query. Using these measurements, the router is trained through supervised fine-tuning followed by group relative policy optimization (GRPO) with the tailored paradigms. Results demonstrate that learned routing can selectively allocate expensive model capacity based on query context and improve the accuracy-energy tradeoff in multi-LLM serving. Across seven benchmark tasks, we also observe a sharp accuracy-energy phase transition among routers, providing practical insights into improving energy efficiency while maintaining LLM performance.

cs.PF↗