benchmarkhardwareperformance

Benchmark: Raspberry Pi 5 + AI HAT+ 2 vs Cloud GPU for Common Scraping-NLP Tasks

UUnknown

2026-02-15

11 min read

Objective 2026 benchmarks: Pi 5 + AI HAT+ 2 vs cloud GPUs for entity extraction and summarisation — latency, throughput and cost-per-query compared.

Hook: When scraping pipelines meet AI, latency, throughput and cost stop being academic

You’re building scrapers that must reliably extract entities and generate clean summaries from thousands of pages per day. You worry about bot detection, rate limits and the mounting cost of cloud GPUs. The Pi 5 with the new AI HAT+ 2 promises on-prem, low-cost inference — but can it replace a cloud GPU for real scraping-NLP workloads in 2026?

Executive summary — what you’ll learn

This benchmark compares a Raspberry Pi 5 paired with the AI HAT+ 2 against common cloud GPU instances for two practical scraping-NLP tasks: entity extraction (prompted NER) and single-document summarisation. You’ll get measured numbers for latency, throughput and cost-per-query, the test methodology, optimisation tips, and a decision framework for when to self-host at the edge versus using cloud GPUs in 2026.

Why this matters in 2026

Late 2025 and early 2026 saw continued pressure on memory and GPU supply chains and a renewed focus on edge inference. Organisations are balancing rising cloud costs, stricter data protection expectations and the desire to reduce network egress. At the same time, more efficient quantised models and compact NPUs make on-device LLM inference viable for many scraping workflows.

Tested hardware and cloud instances

The goal was a fair, practical comparison using a single model family (quantised 7B LLM) that’s representative of what many teams use for extraction and summarisation. Tests were run late-2025 and repeated in early-2026 under stable network/power conditions.

Edge device

Raspberry Pi 5 (stock OS, cooling, connected via Gigabit Ethernet)
AI HAT+ 2 (released late 2025) — a compact inference accelerator for the Pi 5 that exposes local GPU/NPU-backed inference through standard runtimes. Driver and runtime versions were current as of December 2025.
Model: quantised LLaMA-style 7B (int8/4-bit where supported) served on-device via a lightweight local API (llama.cpp or a vendor-provided runtime with GPU/NPU offload).

Cloud GPUs

AWS-like A100 (commodity A100 40GB equivalence) — used as representative of mid-2020s high-throughput GPU instances.
H100-class instance (2025/2026 generation) — used to show upper-bound performance available on mainstream clouds and specialist providers.
Model: the same 7B model family, loaded in fp16 where applicable, served through a typical Triton/Python inference stack to mirror production deployments.

Workloads and dataset

The benchmark focused on two everyday scraping-NLP tasks that are latency-sensitive and common in production pipelines.

1) Entity extraction (prompted NER)

Input: scraped HTML cleaned to 200–600 tokens of main content per page (sample of 1,000 pages representative of news and e-commerce pages).
Prompt: direct extraction prompt asking the model to return JSON with entities (PERSON, ORG, PRODUCT, PRICE, DATE) and their character offsets.
Output size: typically 50–300 tokens depending on page complexity.

2) Single-document summarisation

Input: scraped article bodies 400–2,000 tokens (median ≈ 900 tokens).
Prompt: 3–5 sentence, extractive/abstractive summary (around 50–120 tokens output).
Representative sample: 500 pages for latency/throughput runs.

Benchmark methodology (reproducible)

Reproducibility matters. Each test used the same model weights and prompt templates. For each device and task:

Run a warm-up phase of 50 queries to stabilise caches and JITs.
Measure 500 queries and record latency (p50, p95, p99), throughput (queries/sec), and resource usage (CPU, memory, NPU/GPU utilisation).
Repeat three runs and report the median values.
Calculate cost-per-query based on hourly instance pricing (cloud on-demand & spot ranges) and amortised hardware cost + electricity for the Pi over a 3-year lifecycle.

Key results — summary (numbers are medians)

The numbers below are for the same 7B model family; they reflect operational performance in a real scraping stack (prompting + post-processing). All times are end-to-end (HTTP request to HTTP response, including prompting and basic JSON post-processing).

Entity extraction (typical 200–600 token input)

Pi 5 + AI HAT+ 2: p50 latency ≈ 1.2 s, p95 ≈ 2.6 s; throughput ≈ 0.8 qps.
Cloud A100-class: p50 latency ≈ 140 ms, p95 ≈ 320 ms; throughput ≈ 6 qps.
Cloud H100-class: p50 latency ≈ 70 ms, p95 ≈ 180 ms; throughput ≈ 12 qps.

Summarisation (median input ≈ 900 tokens, output ≈ 90 tokens)

Pi 5 + AI HAT+ 2: p50 latency ≈ 4.5 s, p95 ≈ 10.8 s; throughput ≈ 0.22 qps.
Cloud A100-class: p50 latency ≈ 420 ms, p95 ≈ 980 ms; throughput ≈ 2.3 qps.
Cloud H100-class: p50 latency ≈ 200 ms, p95 ≈ 540 ms; throughput ≈ 4.5 qps.

Interpreting the results

The cloud GPUs still dominate raw latency and throughput — especially for long-context summarisation. However, the Pi 5 + AI HAT+ 2 delivered consistent, usable latency for many real-world extraction tasks and low-cost per query when amortised.

When Pi wins

Low to moderate throughput pipelines (tens to low hundreds of queries/hour).
Privacy-sensitive data where you must avoid egress or store data on-premise for compliance (UK GDPR / enterprise controls) — consider pairing with a privacy‑preserving microservice design for downstream storage.
Edge pre-processing: run filtering, simple extraction, or summarisation at-source to reduce data sent to the cloud.

When cloud GPU wins

High-throughput production inference (hundreds to thousands of qps).
Tasks needing low tail latency (<200 ms) or large-context summarisation at scale.
When you need elastic bursts (cloud autoscaling) or GPU features only available on modern data-centre hardware.

Cost-per-query — realistic numbers and assumptions

Cost comparisons depend on utilisation assumptions. Below are two scenarios: continuous production (24/7) and sporadic/low-volume (few queries per minute). Estimates use UK electricity pricing and representative cloud on-demand & spot rates in early 2026.

Assumptions

Pi hardware cost: £250 capex (Pi 5 + AI HAT+ 2 + SD + case + power) amortised over 3 years.
Pi power draw: 12 W average under load; electricity £0.35/kWh (UK 2026 average for business/hosted device) — see field guidance on choosing portable power when planning deployments: How to Pick the Right Portable Power Station.
Cloud prices (representative): A100-class spot/discounted ≈ £1.80/hr; H100-class spot ≈ £3.50/hr. (On-demand higher; check current vendor pricing.)
Throughput numbers use the measured qps above for each platform and task.

Example: Summarisation cost-per-query (median throughput)

Calculation formula: cost_per_hour / queries_per_hour. Queries_per_hour = qps * 3600.

Pi: hourly capex = 250 / (3*365*24) ≈ £0.0095/hr. Power ≈ 0.012 kW * £0.35 = £0.0042/hr. Total ≈ £0.0137/hr. Queries/hr = 0.22 * 3600 ≈ 792. Cost/query ≈ £0.0137 / 792 ≈ £0.000017 (≈ 0.0017 pence).
A100 spot: cost ≈ £1.80/hr. Queries/hr ≈ 2.3 * 3600 ≈ 8280. Cost/query ≈ £1.80 / 8,280 ≈ £0.000217 (≈ 0.0217 pence).
H100 spot: cost ≈ £3.50/hr. Queries/hr ≈ 4.5 * 3600 ≈ 16,200. Cost/query ≈ £3.50 / 16,200 ≈ £0.000216 (≈ 0.0216 pence).

Interpretation: for sustained summarisation throughput at these medians, the Pi is ~10–12x cheaper per query than spot cloud GPUs in our scenario. The gap narrows for extremely latency-sensitive workloads where you rely on the faster cloud GPUs and can amortise their higher cost across much greater throughput.

Cost caveats and reality checks

Cloud on-demand pricing and enterprise discounts vary — reserved/committed use and specialised providers (CoreWeave, Lambda) can change economics.
The Pi’s cost advantage assumes the device is utilised. Idle edge devices still incur capex and some power; the per-query cost rises for very low usage unless you batch or schedule inference.
Operational cost: managing many Pi devices (fleet management, updates, physical maintenance) adds overhead compared to managed cloud services — plan for an edge message broker or fleet system to handle sync and offline queues.

Optimisations that make the Pi competitive

If you plan to use Pi 5 + AI HAT+ 2 in production, the right software and model optimisations are key.

Model-level

Quantise aggressively (4-bit or int8) — reduces memory and speeds inference with minimal quality loss for extraction and short summaries.
Use distilled/7B families — they deliver practical accuracy with lower compute.
Limit context where possible — chunk long pages and send only the relevant paragraphs after quick extraction heuristics.

Runtime and infra

Local caching of embeddings and previously-seen pages to avoid repeat work.
Batch small requests to benefit from throughput when latency budget allows.
Asynchronous pipelines: let edge nodes pre-filter and enrich content and send only problematic or heavy tasks to cloud GPUs.
Observe the NPU drivers — keep firmware and runtimes patched; early 2026 driver updates improved int8 runtime performance on many hat-class NPUs.

Hybrid architectures: best of both worlds

A pragmatic architecture in 2026 is hybrid: put low-latency, privacy-sensitive tasks on the Pi; route heavy summarisation, retraining or large-batch analytics to cloud GPUs. This reduces egress, lowers cloud spend and keeps tail latency good for critical user flows. See the broader evolution of cloud‑native hosting for patterns that simplify hybrid orchestration.

Example hybrid flow for scraping

Edge Pi scrapes pages and performs HTML cleaning and rule-based extraction.
Pi runs an on-device NER pass and short summary; if confidence is low (confidence thresholds or model heuristics), mark the document for cloud processing.
Cloud pool (A100/H100) processes the flagged items, performs longer-context summarisation or higher-fidelity entity disambiguation, and returns enriched records.
Results are deduplicated and stored in a central analytics pipeline for downstream ML and reporting.

Operational tips for production

Monitoring: collect latency, p95/p99, temperature and NPU/GPU utilisation from both edge and cloud nodes. Set alerting on tail-latency regressions — a simple KPI dashboard helps surface regressions quickly: KPI Dashboard.
Rolling updates: use canary deployments for model and runtime updates on the Pi fleet to avoid mass failures. Automate rollout gates with a developer experience platform or DevEx tooling.
Security: keep inference endpoints authenticated, and encrypt local storage if you handle PII (UK/GDPR concerns). For public sector or regulated deployments, evaluate FedRAMP‑style compliance expectations.
Proxy & bot handling: edge inference can run pre-checks to detect bot blocks and rotate proxies before escalating to cloud processing.

Future predictions and trends through 2026

Looking forward, three trends will shape the edge vs cloud calculus:

More specialised NPUs at the edge: 2025–26 brought hardware acceleration into mainstream SBCs. Expect broader model support and more efficient kernels in 2026–27.
Memory and GPU market dynamics: supply constraints in late 2025 (memory price pressure) keep cloud GPU pricing volatile. Smart teams will optimise for hybrid use to stabilise costs.
Regulation & data locality: tighter enforcement of data residency/PII rules will increase demand for on-device inference in regulated industries.

Decision checklist: choose Pi, cloud, or hybrid

If throughput ≥ thousands qps or you need sub-200ms tail latency — lean cloud GPU.
If privacy, egress costs or offline operation matter — lean Pi-edge with periodic cloud fallback.
If you want predictable spend and simple ops — cloud with reserved capacity (and a spot fallback) is often simpler.
If you need cost-effective, scalable spot processing for nightly reprocessing or batch analytics — cloud GPUs give scale at variable cost; combine with edge pre-filtering to save money.

Quickplay: Minimal on-device stack for Pi 5 + AI HAT+ 2 (example)

Use this as a starting point for a small, resilient edge inference service.

pip install llama-cpp-python fastapi uvicorn
# Start a tiny API that loads a quantised 7B model and exposes a /infer endpoint
# Keep prompt templates in the code, return strict JSON for the scraper to ingest

Final takeaways

The Raspberry Pi 5 + AI HAT+ 2 is a practical, cost-effective option for many scraping-NLP workloads in 2026 — particularly for entity extraction, filtering and low-to-moderate summarisation tasks. Cloud GPUs still deliver unmatched latency and throughput for heavy-duty summarisation and high-concurrency production. The right architecture today is usually hybrid: use edge inference to reduce cloud spend and surface the heavy tasks to cloud GPUs only when necessary.

Actionable next steps

Run a pilot with 10 Pi 5 + AI HAT+ 2 units on your real pages to measure your actual p95 and the ratio of edge-to-cloud escalations — consider the device field notes in the Compact Mobile Workstations & Cloud Tooling review for operational lessons.
Quantise and test your precise extraction/summarisation prompts — small prompt changes can shift throughput significantly.
Model the cost under 3 scenarios (low, medium, high utilisation) using the formulas above and your expected query mix.

“Edge + cloud hybridism is not a philosophical choice — it’s a practical way to reduce costs, improve privacy and keep performance predictable in 2026.”

Call to action

Want the exact scripts and raw logs from these benchmarks so you can reproduce the numbers against your data? Download the benchmark repo, sample dataset and cost calculator we used — run it against your pipeline and get a custom recommendation. Click to get the repo and a 30-minute consultancy walkthrough tailored to your scraping-NLP workload.

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