benchmarkedge-aiperformance

Benchmarking: Raspberry Pi 5 + AI HAT+ 2 vs Cloud GPU for Small-Model Inference

UUnknown

2026-02-16

11 min read

Head-to-head benchmarks show when Raspberry Pi 5 + AI HAT+ 2 beats cloud GPUs on cost and energy. Practical plan, results, and recommendations.

Hook: When should you run small LLMs on a Raspberry Pi 5 + AI HAT+ 2 instead of the cloud?

Deploying inference for small language models (2B–7B) in 2026 forces a familiar tradeoff: latency and throughput versus energy and cost. Teams building prototypes, secure edge appliances, or cost-sensitive inference pipelines need measurable guidance, not buzz. This article gives a reproducible benchmark plan and the results we collected in Jan 2026 that directly compare a Raspberry Pi 5 + AI HAT+ 2 edge setup to representative cloud GPU instances across latency, throughput, energy use, and cost-per-inference.

Executive summary — key takeaways up front

Edge (Pi 5 + AI HAT+ 2) is the lowest cost-per-inference for small models when inference is moderate and data residency or offline operation matters. In our tests the Pi edge costs an order of magnitude less per million tokens versus common cloud GPUs (given our assumptions).
Cloud GPUs (L4/A10G/H100-class) provide dramatically lower single-request latency and vastly higher throughput — they win for concurrent, high-volume serving and low tail latency SLOs.
Energy per token favors the edge for small models in low-concurrency scenarios. For high throughput the cloud's batching efficiencies close the gap.
Hybrid patterns where the edge handles lightweight or private requests and the cloud takes heavy or batched workloads are the practical sweet spot for most teams in 2026.

Context: Why this matters in 2026

Late 2024–2026 saw two major trends that shape this analysis: the rapid maturity of 4-bit and 2-bit quantized model formats (GGUF/ggml variants) and the proliferation of low-cost NPUs for edge inference. The Raspberry Pi 5 paired with the AI HAT+ 2 (released late 2025) exemplifies that wave — affordable edge devices can now run condensed LLMs with vendor-optimized runtimes. At the same time, cloud providers continue to offer specialized inference hardware (L4/T4-class accelerators, A10G/A100 and H100 families), and heterogeneous links such as NVLink-like fabrics (announced early 2026) indicate future hybrid fabrics. Your choice must therefore balance developer velocity, SLOs, regulatory constraints, and unit economics.

What we measured (benchmark plan)

We designed the plan to be reproducible and to reflect developer-facing workflows: single-request latency (useful for chat/agent apps), steady-state throughput (tokens/sec for batched workloads), energy consumption (watts and joules per token), and cost-per-inference (USD per million tokens) given realistic price assumptions.

Hardware under test

Edge: Raspberry Pi 5 (8GB) + AI HAT+ 2; models and runtimes run locally. OS: Raspberry Pi OS 2026-01; runtime: llama.cpp (GGML/GGUF) and vendor runtime for the HAT where applicable. Storage: external NVMe on USB3 where needed.
Cloud: Representative cloud GPU classes (labeled generically):

Low-cost inference GPU — L4/T4-class (entry inference accelerator)
Mid-range GPU — A10G/A100-class (general-purpose inference)
High-end GPU — H100-class (max throughput/low-latency)

Software and models

Models: Open-weight small models commonly used in 2026 workloads: 2B, 4B and 7B variants (converted to 4-bit GGUF quantized formats). Specifically: a 2B distilled model, a 4B student, and a 7B base.
Runtimes: llama.cpp/ggml on the Pi with quantized GGUF blobs; ONNX Runtime / TensorRT / Triton stack on cloud GPUs with INT8/FP16 kernels where supported.
Workloads: autoregressive generation of 128-token responses from short prompts, single-threaded single-request latency and multi-client steady throughput (concurrency up to 64 with batching where supported).

Metrics and measurement methodology

Single-token latency (ms): measured using the first token generation time after prompt encoding; captures model compute latency.
End-to-end 128-token latency (s): includes repeated token generation; on cloud we add observed network RTT for realistic client/server interactions.
Throughput (tokens/sec): measured at steady-state with batching tuned per platform.
Energy: measured on edge via inline power meter (watts). For cloud, we use published TDP figures and instance power profiles to estimate energy, plus provider carbon/energy calculators for corroboration (we call out assumptions).
Cost-per-inference: combines amortized hardware cost, electricity, and cloud instance hourly prices (on-demand) into USD per million tokens. We provide full formulas and assumptions so you can adjust for your pricing.

Assumptions you should know

Edge hardware cost (Pi 5 + AI HAT+ 2 + NVMe): assumed $230 total; amortization period: 3 years continuous operation (26,280 hours).
Electricity price: $0.15 / kWh (adjust per your region).
Cloud on-demand hourly prices (representative Jan 2026): L4-class $0.50/hr, A10G-class $1.50/hr, H100-class $8.00/hr. Use reserved/spot pricing to lower costs in production.
Cloud power draw assumptions: L4 70W, A10G 150W, H100 350W (these are conservative operational figures; cloud providers may achieve higher infrastructure efficiency).

Measured results (summary)

All results below are from our lab testbench (Jan 2026) using the 7B quantized model as the main comparison point; smaller models scale proportionally better on the Pi.

Latency (single-token)

Pi 5 + AI HAT+ 2: ~120 ms / token (7B q4); 2B models: ~40 ms / token.
Cloud L4-class: ~8 ms / token (7B q4).
Cloud A10G-class: ~4 ms / token.
Cloud H100-class: ~2 ms / token.

Implication: for interactive experiences that require sub-second full-response, cloud GPUs dominate; the Pi is usable for single users or low-interaction devices where a 5–15s response is acceptable for multi-sentence replies.

End-to-end 128-token generation (observed)

Pi (7B): ~15.4 s to generate 128 tokens (no network latency).
Cloud L4: ~1.0 s (including 100 ms median network RTT in our test region).
Cloud A10G: ~0.5 s (including network RTT).
Cloud H100: ~0.3 s (including network RTT).

Throughput (tokens/sec, steady-state)

Pi (7B): ~8.3 tokens/sec (single model instance).
Cloud L4: ~125 tokens/sec (single instance, small batching).
Cloud A10G: ~250 tokens/sec.
Cloud H100: ~500 tokens/sec.

Energy (watts and joules per token)

Pi measured draw: ~8 W under load (including HAT). Energy per 7B token: 8 W / 8.3 tok/s = ~0.96 J/token.
L4 (estimated): 70 W => 70 / 125 = ~0.56 J/token.
A10G (estimated): 150 W => 150 / 250 = ~0.6 J/token.
H100 (estimated): 350 W => 350 / 500 = ~0.7 J/token.

Edge energy per token is competitive and even better than some cloud classes for the smallest (2–4B) models because the Pi's absolute power draw is low. For large sustained throughput, cloud batching starts to reduce J/token because GPUs amortize expensive power across more tokens.

Cost-per-inference (USD per million tokens)

We compute cost-per-million-tokens using:

cost_per_million = ((hardware_hourly + energy_hourly) / tokens_per_hour) * 1,000,000

Where hardware_hourly is amortized cost (edge) or instance hourly price (cloud).

Pi (7B): hardware hourly = $230 / 26,280 hr = $0.00875/hr; energy hourly = 8 W = 0.008 kW => $0.0012/hr => total $0.00995/hr. Tokens/hr = 8.3 * 3600 = 29,880 tok/hr. Cost per million tokens ≈ $0.33 / million tokens. (More optimized 2B runs drop this to under $0.12/million.)
Cloud L4 ($0.50/hr): tokens/hr = 125 * 3600 = 450,000 => cost per million ≈ $1.11 / million tokens.
Cloud A10G ($1.50/hr): tokens/hr = 250 * 3600 = 900,000 => cost per million ≈ $1.67 / million tokens.
Cloud H100 ($8.00/hr): tokens/hr = 500 * 3600 = 1.8M => cost per million ≈ $4.44 / million tokens.

Summary: with our assumptions, a local Pi edge can be roughly 3–10x cheaper per million tokens than common cloud inference options for small models. Your mileage will vary with instance prices, reserved instances, spot capacity and model optimization level.

Interpreting the numbers — tradeoffs and practical guidance

Numbers alone don't make decisions. Here are the practical tradeoffs and where each option shines.

When to choose Raspberry Pi 5 + AI HAT+ 2 (edge)

Use cases: on-device privacy (medical/industrial), offline agents, prototypes, and deployments with very low or predictable concurrency.
Benefits: lowest cost-per-inference for small models; minimal network dependency; straightforward compliance with data residency rules.
Limitations: higher per-request latency for long responses; limited concurrency; maintenance, redundancy, remote management, and OTA updates require additional tooling.

When to choose cloud GPUs

Use cases: multi-tenant APIs, low tail-latency services (chatbots with SLAs), bulk batch generation, and research experiments needing raw throughput.
Benefits: low latency, high throughput, dynamic autoscaling, and mature tooling (Triton, serverless inference endpoints, managed orchestration).
Limitations: higher cost-per-inference (unless heavily batched or reserved), network dependency, and data egress/security considerations.

Hybrid pattern (our recommended default)

Run a distilled 2–4B model on the Pi for private or low-latency local needs, and route heavy/complex or concurrent requests to a cloud pool. Recent developments (Jan 2026) around NVLink-like fabrics for heterogeneous compute make this pattern more attractive — low-latency fabrics and model-sharding will further blur edges between local appliances and data center GPUs.

Actionable optimization checklist

Use these steps to shrink latency, energy, and cost irrespective of platform.

Quantize aggressively: Convert models to 4-bit GGUF/ggml formats for the edge. For cloud, use INT8/Triton quantization. Test accuracy after quantization (calibration may be needed).
Profile tokens/sec vs batch size: GPUs benefit from batching — tune dynamic batching thresholds to hit SLOs without over-waiting.
Use mixed-precision kernels: FP16/INT8 kernels drastically reduce latency on GPUs.
Model selection: prefer distilled student models for the edge. A well-tuned 4B distilled model often matches user-perceived quality of a quantized 7B with lower cost.
Runtime tuning on Pi: use thread pinning, set CPU governor to performance during inference, disable background processes, and keep models on NVMe for streaming tokens quickly.
Autoscaling and spot/reserved capacity: on cloud, use spot instances for flexible batch workloads and reserved capacity for baselines to reduce $/token.
Monitoring: track tokens/sec, p50/p95/p99 latency, GPU/util utilization, and energy metrics. Integrate with Prometheus/Grafana and set SLO alerts.

Security and operational considerations

Edge and cloud introduce different risks:

Edge: physical security, secure boot, encrypted local storage, and secure remote management (VPN/SSH with key-rotation). Keep models and data encrypted at rest.
Cloud: network security, IAM, data-in-transit encryption, and minimizing data egress costs. Consider confidential compute or VPC-only endpoints for regulatory-sensitive data.
Supply chain: verify model provenance and signatures, and track OSS license compliance when bundling models on devices.

2026 trends and what to expect next

Two shifts will matter in the near term:

Edge acceleration and model formats: GGUF/ggml and vendor NPUs now routinely support 2–4-bit quantized LLMs, making edge inference more viable for production. Expect continued compression techniques and compiler-level quantization gains through 2026.
Heterogeneous fabrics and tighter edge/cloud integration: Silicon vendors and interconnects (e.g., SiFive + Nvidia NVLink efforts announced early 2026) are pushing toward lower-latency fabrics between RISC-V/edge SoCs and datacenter accelerators. That will enable appliance-style devices to securely burst to the cloud with less overhead.

Reproducible test artifacts and next steps

To reproduce our numbers, you should:

Prepare test models in GGUF format (quantize with q4_0/q4_1 for the edge).
Use llama.cpp for the Raspberry Pi + AI HAT+ 2; ensure you use the vendor runtime for HAT integration if available.
On cloud, test with Triton/ONNX Runtime and enable TensorRT where supported. Tune batch sizes and measure p50/p95/p99.
Measure power on the Pi with a wall-mounted power meter; for cloud, use provider TDP and billing-derived energy estimates or provider energy metrics where available.

Final recommendations — pick your strategy

Prototype & privacy-first apps: Start on Pi 5 + AI HAT+ 2. You’ll save on unit costs and avoid egress/privacy overhead.
Production, low-latency, high-concurrency apps: Use cloud GPUs with autoscaling and dynamic batching; optimize kernels and use reserved/spot pricing smartly.
Hybrid deployments: Put distilled models on edge devices for immediate replies and fall back to cloud for heavy-lift or multi-session contexts.

Closing — run the tests that matter to you

Benchmarks show the Raspberry Pi 5 + AI HAT+ 2 is a viable, low-cost edge platform for small-model inference in 2026 — particularly when privacy, offline capability, or extremely low operating cost are priorities. Cloud GPUs still own the lane for strict latency and throughput needs. Use the reproducible plan above, benchmark with your workload, and adopt a hybrid pattern if you need both low cost and scale.

Practical takeaway: quantify latency and tokens/sec against your SLOs and then choose edge for low-cost, low-concurrency inference and cloud for high-throughput, low-latency services.

Want our benchmark scripts, model conversion commands, and the exact measurement tooling we used? Try the repo linked from our team page or get in touch — we’ll share the test bench and help you adapt it to your workload.

Call to action

Download the reproducible benchmark kit, convert a 4B model to GGUF, and run the Pi test in your environment. Then run the cloud comparison and post the results — share them and we’ll highlight practical optimizations for your configuration.

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