Raspberry Pi 5 + Hailo-8 vs Jetson Orin Nano Super: Which Edge AI Box Wins for Vision Workloads?

For pure vision workloads in 2026, the Raspberry Pi 5 + Hailo-8 HAT is the better edge-AI box for most makers: it pulls less than half the watts of an Orin Nano Super while matching it on YOLOv8n/s throughput, and the Pi platform is cheaper, quieter, and easier to mount in the field. The Orin Nano Super wins the moment you need anything beyond vision — LLM inference, generative models, or CUDA-only workloads — because the Hailo-8 cannot run them at all.

The maker's choice: integrated CUDA stack vs modular accelerator

If you've shopped for an edge-AI box in 2026, you've ended up at the same fork in the road. Path one: NVIDIA's Jetson Orin Nano Super, the refresh of the original Nano, now with a 67 INT8 TOPS sparse rating, 8GB of LPDDR5, and a JetPack 6 stack that runs anything you can compile against CUDA — vision, LLMs, diffusion, robotics middleware, the whole zoo. Path two: a $80 Raspberry Pi 5 + a $70 Hailo-8 AI HAT, totaling around $150-$170, that runs Hailo's own dataflow accelerator at 26 INT8 TOPS, with HailoRT on top of stock Pi OS.

These two boxes do not compete on paper. The Orin Nano Super has 2.5× the rated TOPS and a real GPU. But the Hailo-8 is a vision-first NPU that nukes its rated TOPS on YOLO-class workloads — the dataflow architecture skips the bottlenecks Jetson hits in memory bandwidth — and at 2.5W typical it's an order of magnitude more efficient. So which one actually wins on your bench depends on what you're shipping. We'll get into the numbers.

This piece compares the two on the workloads makers actually run: object detection (YOLOv8n/s/m at 640×640), small-LLM inference (Phi-3-mini, Llama 3.2 3B), sustained thermals, dev-experience friction, and total-cost-of-ownership including the parts you forget about — case, fan, SD card, PoE.

Key takeaways

• Pi 5 + Hailo-8 wins for fixed-vision pipelines (camera in, detections out): same FPS as Orin Nano Super on YOLOv8n/s, ~2.5W typical vs 12-15W, $150 vs $249.
• Orin Nano Super wins the moment you need flexibility — LLMs, custom ops, anything CUDA — because the Hailo-8 can't run them.
• YOLOv8m is where Jetson pulls ahead — the larger backbone pushes past the Hailo-8's on-chip memory budget, hurting batch-1 throughput.
• Power budget matters more than peak TOPS for battery / solar / PoE deployments. Hailo wins these fights cleanly.
• Dev experience favors NVIDIA today, but only marginally — HailoRT 4.x is mature in 2026 and the model zoo covers most YOLO/EfficientDet/MobileNet variants out of the box.

How do Hailo-8 (26 TOPS) and Orin Nano Super (67 TOPS) actually compare in real workloads?

The TOPS numbers are misleading on both sides. Hailo quotes 26 INT8 TOPS at 2.5W — a real-world number, measured at the chip's typical workload. NVIDIA quotes 67 sparse INT8 TOPS for the Orin Nano Super, which is the marketing-friendly number that requires both INT8 quantization and 50% structured sparsity to hit. The dense INT8 number is closer to 33-34 TOPS. So the actual gap is 26 vs ~34 dense INT8 TOPS, not 26 vs 67.

That still puts Jetson ahead on paper. But peak TOPS doesn't translate to FPS unless you're memory-bandwidth-rich and have models that fit on-chip. The Hailo-8 is a true dataflow architecture: it streams activations through a fixed pipeline of compute clusters, with very little DRAM traffic during inference. That makes its on-chip throughput close to its rated peak for vision models that compile cleanly onto its dataflow graph (YOLO, EfficientDet, ResNet, MobileNet — all of which Hailo supports out of the box).

The Jetson Orin Nano Super, by contrast, is a small Ampere GPU sharing 8GB of LPDDR5 across CPU and GPU. Memory bandwidth is 102 GB/s — fine for a $249 board, but you'll hit it on anything with large feature maps or batch>1. So Jetson's effective throughput on YOLOv8n is roughly 60-70% of its theoretical peak, while Hailo-8 hits ~85-90% of its rated peak.

Net: on YOLO-class detectors, the boxes are closer than the spec sheet implies. On anything memory-bound or anything outside the vision domain, Jetson stretches the lead.

What FPS do you get on YOLOv8n / YOLOv8s / YOLOv8m at 640×640?

Numbers from Hailo's published benchmarks (Hailo-8 on Pi 5, HailoRT 4.18, INT8) and NVIDIA's published JetPack 6 numbers (Orin Nano Super in Super mode, TensorRT 10, INT8) — both at 640×640, batch 1, single-stream, as of 2026:

The pattern is consistent: Hailo wins the small-model regime where the model maps cleanly onto its dataflow pipeline. Jetson wins as soon as the model gets big enough that the Hailo-8 starts spilling activations or the model isn't supported in the Hailo Model Zoo. On the YOLOv8l line, the Hailo-8 can run a heavily quantized variant but it's not a fair comparison — most makers reach for YOLOv8m as the upper bound on Hailo before they switch to a Hailo-8L or a beefier accelerator.

If your bench runs YOLOv8n or YOLOv8s — which covers maybe 80% of maker-grade detection projects (people-counting, package detection, wildlife cams, parking-lot occupancy) — the Hailo wins or ties. If you're at YOLOv8m or chasing higher-mAP variants, Jetson takes it.

How does power draw and thermals compare under sustained load?

This is where the boxes diverge most. Steady-state numbers, idle and under sustained YOLOv8s inference, measured at the wall (so they include PSU losses):

The Hailo-8 itself draws about 2.5W typical, 3.5W peak — the rest of the Pi 5 + Hailo total is just the Pi (Cortex-A76 cluster, RAM, USB controller). The Orin Nano Super in Super mode runs the GPU at 1020 MHz and the CPU at 1.7 GHz, with a configurable 7W / 15W / 25W power profile. To match Hailo's FPS on YOLOv8m you need the 25W profile.

Thermals follow power: the Pi 5 + Hailo combo runs cool enough on a basic aluminum heatsink that you can leave it in a sealed enclosure outdoors. The Orin Nano Super throttles within 60-90 seconds without active cooling at 25W; the dev kit's bundled fan handles it, but a custom carrier board needs at least a 30mm blower or a beefy heatsink with ducted airflow. For PoE deployments — which are the bread and butter of fixed cameras — the Pi 5's ~8W ceiling fits comfortably under the 802.3af 12.95W budget, while the Orin Nano Super at 15W needs 802.3at PoE+. That's a real BOM difference: PoE+ injectors are 2-3× the price of PoE.

What about LLM inference (Phi-3, Llama 3.2 3B) — can either box do dual-purpose?

This is where Jetson stretches its legs and Hailo-8 disappears. The Hailo-8 is vision-only — its compiler does not target transformer-style attention-heavy graphs in any production path as of 2026. You cannot run Phi-3-mini on the Hailo-8.

The Orin Nano Super can. With the 8GB unified memory and the JetPack 6 LLM container (vLLM-on-Jetson, llama.cpp with CUDA, or NVIDIA's MLC-LLM build), here's what you get:

These numbers are usable. 18-22 tok/s is faster than human reading speed, so you can build a local voice assistant, a doc-summary bot, or a retrieval-aware-generation pipeline on a single Orin Nano Super. Combine that with TensorRT vision in the same process — multi-modal robotics work, basically — and you have a single box that does both.

The Pi 5 + Hailo-8 cannot do this. You can run llama.cpp on the Pi 5 CPU itself (the Hailo HAT does not participate), and you'll get about 4-5 tok/s on Phi-3-mini Q4 — fine for nightly batch jobs, painful for interactive use. So if dual-purpose (vision and LLM) is on the roadmap, the answer is Jetson.

How painful is the dev experience (HailoRT vs JetPack/TensorRT)?

Five years ago the Jetson dev experience was the reason you bought a Jetson — nothing else came close to a CUDA-on-edge story. In 2026 the gap has narrowed but it's still real.

HailoRT (Hailo's stack):

• Install via apt on stock Raspberry Pi OS Bookworm. ~5 minutes.
• Models compile from ONNX with the Hailo Dataflow Compiler. The compiler runs on x86 host (not on the Pi), produces a .hef file you copy to the Pi.
• The Hailo Model Zoo has pre-compiled .hefs for ~80 common models — YOLOv5/v8, EfficientDet, ResNet, MobileNet, OCR variants. If your model is in the zoo, you're running in 10 minutes flat.
• If your model isn't in the zoo, you compile it. The compiler is decent but not perfect — some custom layers fail and need replacement. Plan a half-day for a non-standard model.
• Python and C++ APIs. Python is the easy path; the inference loop is six lines.

JetPack 6 (NVIDIA's stack):

• Flash from a host machine via SDK Manager (~30-60 minutes, including SDK install). Has gotten easier than the JetPack 4 era but still slower than apt.
• TensorRT is the production runtime; you convert from ONNX or PyTorch with trtexec. TensorRT compilation can take 10-20 minutes for a YOLOv8m. INT8 calibration adds another 30-60 minutes.
• Native CUDA means anything that runs on a GPU runs here, with caveats — sometimes at painful effort. Custom ops, PyTorch features, niche libraries. The flexibility is unmatched.
• Containers via NGC. The LLM container "just works" for popular small models; vision containers ship pre-baked DeepStream pipelines.

For a fixed-vision project that uses a stock model: Hailo is faster start-to-finish, mostly because the Hailo Model Zoo is a real shortcut. For a research / prototype / mixed workload: Jetson is the safer bet because you'll inevitably need to do something custom and CUDA is the broadest target.

Total cost of ownership: Pi 5 + Hailo-8 HAT vs Orin Nano Super dev kit

What you actually spend in 2026 USD, ready-to-run:

So the Pi 5 + Hailo-8 box ships at ~$225 to a customer site vs ~$349 for the Orin Nano Super box. At quantity 10+ for a fleet deployment that's a $1,200+ delta — meaningful for school districts, makerspaces, small businesses, or startups stretching a hardware budget.

If you need three of these for a small office, the Pi path is $675 vs $1,047. For a single dev who wants the most flexible box on their desk: spend the $124 extra on the Jetson and you get LLM ability for free.

Spec-delta table: TOPS, RAM, power, MSRP, supported runtimes

Benchmark table: YOLOv8n/s/m FPS, Phi-3-mini tok/s, idle vs load watts

Consolidated from the per-section tables for at-a-glance comparison:

Verdict matrix: who should buy which

Get a Pi 5 + Hailo-8 HAT if:

• Your workload is fixed-purpose vision: object detection, classification, OCR, presence detection, license-plate reading.
• Power, heat, fan noise, or PoE budget matter — outdoor, sealed enclosure, battery, solar, fleet of 10+.
• Your model is in the Hailo Model Zoo or compiles cleanly from ONNX.
• You want the cheapest, lowest-friction box for a maker project at $150-$225 all-in.
• You already run a Pi-based stack (Home Assistant, Frigate, ROS) and want to add NPU vision without changing platforms.

Get an Orin Nano Super if:

• You need vision and LLMs, or vision and generative models, on the same box.
• You need CUDA — for custom ops, PyTorch in the loop, MoveIt2/Isaac, or any niche library.
• Your model isn't in the Hailo zoo and you don't want to debug Hailo Dataflow Compiler errors.
• You're prototyping and don't yet know what you need to run.
• You need YOLOv8m or YOLOv8l throughput.

Get something else (Hailo-8L, Hailo-10H, Orin NX 16GB, Coral M.2) if:

• The Hailo-8 isn't enough but the Orin Nano Super is too power-hungry → Hailo-10H (40 TOPS, similar Hailo-8 efficiency profile).
• You need >8GB unified memory for bigger LLMs → Orin NX 16GB.
• Your detector is INT8-compatible and you've outgrown a Pi 4 + Coral → Pi 5 + Hailo-8L (cheaper than full Hailo-8).

Common pitfalls

• Don't trust the sparse-67-TOPS number on Jetson without verifying your model uses structured sparsity. Most off-the-shelf YOLO weights don't, so the dense ~34 TOPS is what you'll actually see.
• The Hailo-8 needs the official Hailo HAT, not a generic M.2 carrier. The HAT exposes the Pi 5's PCIe Gen 3 lane correctly; some early third-party carriers run at Gen 2 speeds and bottleneck the input pipeline.
• The Orin Nano Super dev kit ships in 7W mode out of the box. You must run nvpmodel -m 0 to enable the 25W "Super" performance mode. People benchmark in default mode and conclude the box is slow — it's not, you just didn't turn it on.
• HailoRT models are tied to a specific HailoRT version. If you upgrade HailoRT, you may need to recompile your .hefs. Check release notes before upgrading on a deployed fleet.
• Both boards thermal-throttle a SD card before they throttle the CPU. If you're running heavy sustained vision, use NVMe (Pi 5) or the included NVMe slot (Orin Nano Super) — SD cards die fast under continuous logging.

When NOT to use either

If your model is YOLOv8x or anything ResNet-152-class at full resolution, neither box is enough. You're shopping in the Orin NX 16GB / Jetson AGX Orin / RTX 4060 mini-PC tier — different conversation entirely.

If your project is purely a single MQTT sensor with an IR camera trigger, both boxes are overkill. A Pi Zero 2 W with TFLite Micro and a Coral USB will do the job at $50 all-in.

Bottom line

For 2026 maker builds, the Pi 5 + Hailo-8 HAT is the better default for vision pipelines. It's cheaper ($150 vs $249), runs at half the power, fits PoE budgets cleanly, and matches or beats the Orin Nano Super on the small-model YOLO regime that covers most projects. The Jetson is the right call only when you need flexibility — LLMs on the same box, CUDA, PyTorch in the loop, or models the Hailo can't run. Buy the Hailo box when the workload is settled and vision-only; buy the Jetson when you don't yet know what you'll run, or when "vision-only" is a lie you tell yourself before you bolt on a chatbot.

Related guides

• Hailo-8 vs Coral USB: Which Edge AI Stick Wins in 2026?
• NVIDIA Jetson vs Hailo-8: Edge AI Box Comparison
• Best Single-Board Computer for Object Detection in 2026
• Best 24GB GPU for Local LLM Inference in 2026

Sources

• Hailo-8 published benchmarks (hailo.ai), HailoRT 4.18 release notes, 2026
• NVIDIA Jetson Orin Nano Super JetPack 6 benchmarks (developer.nvidia.com), 2026
• Phoronix Pi 5 + Hailo-8 review series, January 2026
• LocalLLaMA edge-inference threads, 2025-2026
• Power measurements: Kill A Watt P3 P4400 at the wall, our bench, March 2026