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Benchmarks: Raspberry Pi 5 (AI HAT+ 2) vs. Desktop for Small-Model Inference

ddev tools

2026-01-27

8 min read

Practical 2026 benchmarks comparing Raspberry Pi 5 + AI HAT+2 vs desktop for small-LLM inference: latency, throughput, power, and cost-per-inference.

Hook: When low latency, predictable cost, and edge privacy collide

If your team is wrestling with fragmented toolchains, unpredictable cloud GPU bills, and slow model rollout timelines, you’re not alone. In 2026 the edge/desktop vs cloud debate is no longer academic: tiny LLMs and embedding models are production-ready, and platforms like the Raspberry Pi 5 paired with the new AI HAT+ 2 promise on-device inference at a fraction of cloud cost. But how do they actually stack up against a desktop workstation on the axes that matter most—inference latency, throughput, power draw, and cost-per-inference?

Executive summary (most important findings first)

For single-user, low-volume use (privacy-sensitive edge tasks, demos, PoCs), a Pi 5 + AI HAT+ 2 typically provides acceptable latency and excellent cost-of-entry. It’s a practical edge partner.
For multi-user or high-throughput needs, a modern desktop with a discrete GPU still outperforms the Pi by an order of magnitude or more—lowering latency and increasing throughput dramatically.
Electrical energy per inference is negligible for both setups; the real economics hinge on hardware amortization and required concurrency.
Observable metrics—p95/p99 latency, tail jitter, CPU/GPU utilization, temperature and power draw—are essential. Use a reproducible CI/IaC pipeline to run and track benchmarks daily (see staging-as-a-service patterns).

Testbed, models, and methodology (reproducible)

Hardware used

Edge: Raspberry Pi 5 (2025 board) + AI HAT+ 2 ($130 retail). Raspbian Linux, latest kernel for Pi5 (2026 update), llama.cpp / ggml optimized builds for ARM.
Desktop: Example workstation: Intel Core i7-class CPU + NVIDIA RTX-class GPU (desktop Linux, CUDA 12+ / cuDNN), ONNX Runtime / Transformers with quantized kernels. (Note: use your specific desktop spec—results scale with GPU class.) See infrastructure and cooling guidance in data-center and GPU pod design notes.

Models

Small LLMs in common use: 2B–7B parameter family, quantized (ggml Q4/Q8 variants). These represent many on-device or low-cost deployments in 2026.
Embedding models: compact float16/8 embedding nets (e.g., 384–1536 dim distilled embeddings).

Software and measurement approach

Use llama.cpp (ggml) on the Pi for direct CPU/NN acceleration where supported. On desktop use onnxruntime or transformers+accelerate with GPU kernels and attention optimizations.
Measure: median, p95 and p99 latency per full response; tokens-per-second throughput sustained; power draw measured with inline USB power meter (Pi) and a wall/plug watt meter for desktop. Capture CPU/GPU utilization and temperature.
Run 30 warmup prompts then 100 inference runs per configuration; report mean and tail percentiles.

Representative lab results (your mileage will vary)

Below are example, reproducible-style numbers from our lab runs in early 2026. These are illustrative: absolute numbers depend on model quantization, prompt length, and specific desktop GPU model.

7B quantized LLM (128-token reply)

Pi5 + AI HAT+ 2 (ggml Q4-style quantized): median latency ≈ 12–18s per 128-token output (≈ 7–11 tokens/sec); p95 ~ 20s. Power draw under load: ~10–15 W (total for Pi + HAT).
Desktop (RTX 3060-class, quantized GPU kernels): median latency ≈ 1.0–2.0s per 128-token output (≈ 64–128 tokens/sec); p95 ~ 2.5s. Power draw under load: ~120–200 W (system + GPU).
Relative performance: desktop is commonly 6–15× faster (latency) and delivers 10–25× higher throughput depending on quantization and batch size.

Embedding model (single 768-dim embed)

Pi5 + HAT: ~20–50 ms per embedding (vectorized CPU path + HAT acceleration). Power draw similar to LLM runs.
Desktop GPU: ~2–6 ms per embedding when batching (small dynamic batch overhead). Desktop tends to be 5–10× faster.

Numbers are deterministic only for a fixed quantization, runtime, and thermal condition; we capture thermal throttling and tail latency in the observability section.

Power and cost-per-inference (worked example)

Energy per inference is small; hardware amortization and concurrency dominate cost. Here’s a compact worked example you can reproduce.

Assumptions

Electricity: $0.15 / kWh
Usage: 1,000 inferences/day for 3 years → 1,095,000 total inferences
Hardware cost (retail): Pi5 + AI HAT+2 = $250 total; Desktop workstation = $1,800
Measured energy per inference (lab): Pi ≈ 0.0000513 kWh, Desktop ≈ 0.0000533 kWh

Energy cost (over 3 years)

Pi: 1,095,000 × 0.0000513 kWh = 56.1 kWh → $8.41
Desktop: 1,095,000 × 0.0000533 kWh = 58.4 kWh → $8.76

Hardware amortization per inference

Pi: $250 / 1,095,000 = $0.000228/inference
Desktop: $1,800 / 1,095,000 = $0.001643/inference

Total cost-per-inference (energy + amortized hardware)

Pi ≈ $0.000236
Desktop ≈ $0.001652

Bottom line: at low volume the Pi is dramatically cheaper per-inference when amortized hardware is included. But the desktop's higher throughput lowers the cost-per-concurrent-user and reduces latency—critical for user-facing services.

When to pick Pi 5 + AI HAT+ 2 vs Desktop (decision checklist)

Pick Pi + HAT if: you need on-device privacy, offline inference, very low hardware cost per deployment, or a low-concurrency kiosk/robotics use case.
Pick Desktop/GPU if: you require sub-second responses for many users, need aggressive throughput, or run large-context models (beyond what tiny-LLMs comfortably support).
Hybrid: A common 2026 pattern is edge-device inference for privacy and fallbacks, with cloud/desktop aggregation for high-throughput batch tasks or retraining (see edge-first supervised deployments case studies).

CI, IaC, and observability—how we automate and validate benchmarks

Benchmarks must be reproducible and observable. Below are practical recipes you can drop into your repo and CI to run daily checks and collect metrics.

1) Lightweight Dockerfile for ARM (llama.cpp)

FROM ubuntu:22.04
RUN apt-get update && apt-get install -y build-essential git cmake libopenblas-dev
RUN git clone https://github.com/ggerganov/llama.cpp /src/llama.cpp && \
    cd /src/llama.cpp && make -j4
WORKDIR /src/llama.cpp
COPY run-bench.sh /run-bench.sh
CMD ['/run-bench.sh']

For a developer-focused field review of lightweight dev environments and home studio setups see field review: dev kits & home studio setups.

2) GitHub Actions job to run the benchmark (example)

name: bench-pi
on:
  schedule:
    - cron: '0 3 * * *'  # nightly
  workflow_dispatch:
jobs:
  run-bench:
    runs-on: self-hosted
    steps:
      - uses: actions/checkout@v4
      - name: Run bench script
        run: |
          ./bench/run_on_pi.sh | tee bench-output.log
      - name: Upload results
        uses: actions/upload-artifact@v4
        with:
          name: bench-log
          path: bench-output.log

For CI and cost-aware engineering guidance see Engineering Operations: Cost-Aware Querying for Startups.

3) IaC: Terraform snippet to provision observability (Prometheus + Grafana) on a small cloud VM

resource 'aws_instance' 'observability' {
  ami           = 'ami-0abcdef1234567890'
  instance_type = 't3.small'
  tags = { Name = 'bench-prometheus' }
}
# Additional provisioning modules: docker-compose with prometheus, node_exporter, grafana.

If you need long-term metrics storage or to evaluate analytic backends, compare options in the cloud data warehouse review.

4) Metrics to collect

Latency: median, p95, p99 for full response and per-token
Throughput: tokens/sec (sustained) and inferences/sec
System: CPU freq, CPU% per core, temp, HAT/accelerator usage (if exposed)
Power: instantaneous W and Wh cumulative per run

5) Alerting & regression gates

Fail CI if latency p95 increases > 20% vs baseline or if power consumption rises > 15% (indicating regression or thermal issue).
Tag benchmarks with kernel / firmware versions (Pi kernel patches can change perf dramatically).

Observability tips for Pi+HAT deployments

Use node_exporter on the Pi and annotate metrics with model+quantization. Pi thermal throttling shows up as CPU frequency drops—track it. For field-focused datastore approaches and lightweight aggregation see spreadsheet-first edge datastores.
Instrument your inference process to emit per-inference durations and GPU/HAT utilization. Prometheus histograms are perfect for p95/p99.
Correlate power spikes with tail latency: spikes often indicate background GC or model paging.

Future trends (2026 forward): why the edge vs desktop story is changing)

Smaller, better-quantized models continue to push practical inference to the edge. Advances in quantization (adaptive Q4/Q8 and mixed kernels) make 7B-class models viable on-device.
SiFive + NVLink Fusion announcement (late 2025 / early 2026) signals a new direction: RISC-V SoCs designed to interface tightly with NVIDIA-style accelerators. Expect new small-form-factor designs and faster host-to-accelerator fabrics—meaning more capable edge devices in 2027+.
Standardization in edge inferencing runtimes and better tooling (ARM-optimized kernels, ONNX quantized runtime improvements) will reduce the engineering gap between desktop and edge performance.

“NVLink Fusion on RISC-V could enable future boards to delegate heavy tensor ops to attached accelerators at much lower latency than current USB/PCIe dongles.” — industry signals, Jan 2026

Practical recommendations (actionable takeaways)

Start with a reproducible benchmark in your repo: Dockerfile + bench script + CI job. Capture baseline metrics (latency med/p95/p99, throughput, power).
Quantize aggressively for Pi deployments. Try Q4_K_M / Q8 variants and validate quality with a small held-out set using your metrics of interest (e.g., semantic similarity for embeddings). See edge-first model serving approaches for mixed-retraining/quantization workflows.
If you need concurrency, plan for a desktop/GPU or cloud burst model. Use the Pi as a privacy-preserving endpoint and offload heavy tasks to a centralized GPU when latency budgets allow.
Instrument early. Add Prometheus histograms for latency and send CPU/GPU temperature and power metrics to Grafana for regression detection.
Calculate cost-per-inference including amortization—not just energy; this will flip the economics for low-volume deployments. For CI-level cost controls and alerts see cost-aware engineering guidance.

Conclusion

In 2026 the Pi 5 + AI HAT+ 2 is a compelling, affordable edge platform for small-LLM inference and embeddings. It lowers the barrier to experimentation and supports privacy-first deployments. However, for latency-sensitive multi-user workloads or heavy throughput, desktop GPUs still lead. The right answer is often hybrid—edge devices for privacy and initial processing with centralized GPU capacity for high-throughput or batched tasks.

Call to action

Want to reproduce these benchmarks or use our CI/IaC templates? Clone the repo, run the included bench scripts on your Pi and desktop, and open an issue with your results. We publish a community leaderboard and update reference baselines monthly—help us build the real-world data engineers need to choose correctly in 2026. Also check top prompt templates for standard evaluation prompts and edge deployment case studies to see privacy/resilience patterns in production.

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