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Jetson Orin Nano Super vs Raspberry Pi 5: Real Edge-AI Benchmarks (2026)

By SpecPicks Editorial · Published Apr 24, 2026 · Last verified Apr 24, 2026 · 11 min read

The NVIDIA Jetson Orin Nano Super delivers 21.75 tok/s on Qwen2.5-7B (INT4) and ~31 tok/s on DeepSeek-R1-Distill-Qwen-1.5B (Q4) for $249, versus the Raspberry Pi 5 8GB at 5.80 tok/s on Llama 3.2 3B (Q4_K_M) for $80. On an any-model-you-want basis the Orin Nano Super is roughly 4–6× faster for LLM inference and ~12× faster for classical vision workloads like YOLOv8 — but the Pi 5 is still the better buy for non-AI edge projects. Here is what the benchmark data actually supports.

Key takeaways

• If you're building an always-on LLM endpoint, the Orin Nano Super is the only device in its price class that runs 7B-class models above conversational speed (>20 tok/s). The Pi 5 tops out around 2 tok/s on Mistral 7B — usable for batch jobs, not chat.
• Under 3B parameters, the gap narrows dramatically. Pi 5 runs Llama 3.2 3B at 5.80 tok/s and BitNet B1.58 2B at 8 tok/s — perfectly serviceable for RAG and classification. Spending 3× more on the Jetson for a 2B model is overkill.
• For non-AI edge work — home automation, media servers, retro gaming, GPIO projects — the Pi 5 is still the answer. Its 10,000-project software ecosystem, broader Linux compatibility, and $80 entry point are untouchable.
• The Jetson's real unlock is the CUDA stack. TensorRT, DeepStream, Isaac ROS, and the cuDNN-accelerated llama.cpp build give you genuine datacenter-class tooling in a 25 W envelope. Pi 5 with the Hailo-8L AI HAT (+$70) is the closest software-compatible competitor, but the toolchain is narrower.
• Neither device replaces a real GPU for model development. Both are inference-optimized. If you're training or fine-tuning, put the budget toward an RTX 4090 or a 5090 and keep these boards for deployment.

Orin Nano Super at a glance: what $249 now buys

The Orin Nano Super isn't new silicon — it's a firmware unlock of the 2023 Orin Nano 8GB. NVIDIA bumped the GPU clock to 1,020 MHz, raised the CPU from 1.5 GHz to 1.7 GHz, and unlocked a 25 W MAXN Super power profile on a module that previously capped at 15 W. The result: 67 TOPS (INT8 sparse) from the same SoC that used to ship as "40 TOPS." Existing Orin Nano Dev Kit owners got the upgrade for free via a JetPack update in December 2024. New buyers get the same module at the lower $249 MSRP.

Source: NVIDIA Jetson Orin Nano Super Developer Kit spec sheet, Raspberry Pi Foundation benchmarks, our Raspberry Pi 5 8GB benchmark page. Geekbench 6 multi-core for the Raspberry Pi 5 8GB = 1,604 per the Raspberry Pi Foundation's own published numbers.

How does Jetson Orin Nano Super compare to Raspberry Pi 5 in AI workloads?

Across every benchmark we have on file, the Orin Nano Super wins — usually by 4–12×, depending on how well the workload parallelizes on its 1,024 CUDA cores.

LLM token generation (single-user, Q4 class quantization)

Hard numbers in bold are from our ai_benchmarks catalog. Sources: Cytron's DeepSeek-R1 walkthrough (24.20 tok/s), DEV Community write-up by Ajeet Raina (30.98 tok/s with an optimized Ollama + Docker setup), NVIDIA Jetson AI Lab developer forum thread (21.75 tok/s on Qwen2.5-7B INT4 via MLC), and the aggregated Pi 5 numbers from aidatatools' Pi 5 Ollama benchmarks, Jeff Geerling's 16GB Pi 5 review, and Stratosphere Laboratory's BitNet test. "Est." entries interpolate between published Orin Nano Super numbers using the standard memory-bandwidth-limited scaling model; treat them as ±15 %.

The honest headline: the Orin Nano Super is the only sub-$300 device that clears 20 tok/s on a 7B-class model. That's the threshold where a local assistant feels responsive instead of apologetic.

Classical CV / vision workloads

Vision is where the Jetson's CUDA+TensorRT stack earns its keep. YOLOv8n at 640×640 hits 32–40 FPS on the Orin Nano Super with TensorRT FP16, per NVIDIA's Jetson AI Lab benchmarks. A Raspberry Pi 5 running the same model on CPU manages ~2.7 FPS; even with the Hailo-8L AI HAT+ (Raspberry Pi's own $70 AI accelerator), it reaches ~30 FPS — matching the Jetson, but only for Hailo-supported models.

What this means in practice

• Real-time video analytics on 1–2 streams at 1080p → Orin Nano Super, easily. Pi 5 + Hailo-8L can do it for one stream but chokes on two.
• Local chat assistant (7B model, conversational latency) → Orin Nano Super only.
• Keyword-spotting, small classifiers, 1B-class LLMs for router-style prompt dispatch → Pi 5 is fine.
• Embedded robotics with ROS2 → Orin wins decisively; NVIDIA Isaac ROS has GPU-accelerated perception nodes that the Pi simply cannot run.

Can you actually run Llama 3.1 8B or Qwen2.5-7B on the Orin Nano Super?

Yes, with caveats. The 8 GB unified memory is the gating factor — the module shares RAM between the CPU and GPU, so anything you allocate for the model reduces what's available for OS + CUDA workspace.

Practical working matrix for 8 GB Orin Nano Super (MAXN Super power mode):

If you need anything larger than 8B at reasonable quant, the Orin NX 16GB ($599) or the AGX Orin 32/64GB is the correct upgrade path — not the Nano Super with swap.

Runtime recommendations for the Orin Nano Super

• MLC-LLM: consistently the fastest runtime on Jetson for 7B-class models in 2026. The Qwen2.5-7B at 21.75 tok/s number above was MLC. Setup is more work than Ollama — you compile the model against the Orin's specific TVM target.
• Ollama via NVIDIA's Ollama container (DEV Community walkthrough): easiest path, within ~10 % of MLC on small models. The Docker image NVIDIA ships handles CUDA automatically.
• llama.cpp with CUDA: middle ground. Best if you want to experiment with custom quants or grammar-constrained decoding. Use the Jetson-specific CUDA backend build (GGML_CUDA=1).
• vLLM: works on Jetson in 2026 but you lose the memory-efficiency advantage on an 8 GB board — vLLM's PagedAttention shines at batch > 1. For single-user use MLC or Ollama.

What does Raspberry Pi 5 actually do best?

The Pi 5 is not an AI board, and treating it like one sets you up to overpay and under-perform. Where it dominates:

• Home automation hubs (Home Assistant OS, ESPHome, Zigbee2MQTT) — 90 % of the installed base.
• NAS / media server builds with the PCIe Gen2 ×1 slot and an NVMe HAT. Cheapest path to a silent 2 TB home NAS.
• Retro gaming handhelds and arcade cabinets (RetroPie, Batocera, EmulationStation).
• GPIO + sensor + I²C projects — 40-pin header, well-documented hats, massive hobby community.
• Classrooms and certifications — the Raspberry Pi curriculum is the default in schools.
• Kubernetes / Docker clusters for fun — see our 8-node Pi cluster guide.

Even on AI, the Pi 5 is surprisingly capable for small models. At Q4 quantization, Llama 3.2 3B runs at 5.80 tok/s — faster than most people type. If your use case is an offline classifier, a 1-2 request-per-minute RAG endpoint, or a sub-agent on a mesh, the Pi 5 is good enough and a third the price.

Where the Pi 5 falls over for AI

• Anything ≥ 7B parameters: 2 tok/s is technically working, practically not. You're looking at 30+ seconds for a short paragraph.
• Vision at >5 FPS without Hailo-8L: VideoCore VII has no ML acceleration. Plain-CPU YOLO on the Pi 5 is a demo, not a deployment.
• Any workload that requires CUDA — PyTorch/TF with CUDA, Whisper-Turbo, most diffusion pipelines, TensorRT. These simply don't exist on Pi.
• Concurrency: with 4 cores at ~1,600 Geekbench 6 multi, the Pi 5 is single-tenant in practice.

Power, thermals, and real-world cost

Orin Nano Super sustained draw:

• 7 W mode: ~9 W at wall with typical PSU losses, throttled to ~12 TOPS.
• 15 W mode (default): 17–18 W wall, ~40 TOPS sustained.
• 25 W MAXN Super: 28–30 W wall, the full 67 TOPS — but ships with a recommended 5A USB-C PD supply and the included active cooler is mandatory. Without adequate airflow the SoC hits 94 °C and throttles back to 15 W within ~3 minutes.

Raspberry Pi 5 sustained:

• Idle: 3 W.
• 100 % CPU load with active cooler: 8–9 W.
• With Hailo-8L AI HAT+ under AI load: ~12–13 W.

Cost-per-token-per-second (Qwen2.5-7B class workload):

• Orin Nano Super @ 21.75 tok/s / $249 = $11.45 per tok/s.
• Pi 5 8 GB + Hailo-8L (no 7B LLM path) = not comparable; Hailo runs compiled-graph vision models, not GGUF LLMs.
• Pi 5 @ 2 tok/s on Mistral 7B / $80 = $40 per tok/s (and still unusable for chat).

The Orin wins per-dollar-per-token for LLM work by a factor of roughly 3.5×. For anything where tokens don't matter, the Pi's $80 entry fee is unmatched.

Buying the hardware (what we actually recommend)

NVIDIA Jetson Orin Nano Super Developer Kit — $249

The official SKU. 347 verified Amazon reviews, 4.2 stars. Includes the carrier board, Orin Nano 8 GB module, and the reference active cooler. You still need to add a microSD (16 GB+ for boot), an NVMe SSD (256 GB+ recommended — JetPack 6 is large), and a 5 V / 5 A USB-C PD supply if you plan to run MAXN Super. Expect to spend ~$80 more to get the full rig on its feet.

View on Amazon →

Price sourced from Amazon.com. Last updated Apr 24, 2026. Price and availability subject to change.

See our full Jetson Orin Nano Super benchmark page →

Raspberry Pi 5 8GB — $80 (bare board) / ~$200 kit

The board itself is $80 MSRP but almost never ships in isolation — CanaKit, Vilros, and the official Pi Imager kit bundles are the norm for most buyers. The Raspberry Pi 5 8GB Amazon SKU currently sits at 4.7 stars across 2,750+ reviews.

View on Amazon →

Price sourced from Amazon.com. Last updated Apr 24, 2026. Price and availability subject to change.

See our full Raspberry Pi 5 review →

Add the Hailo-8L AI HAT+ if you want AI on the Pi

If you're already in the Pi ecosystem and need meaningful AI performance, the $70 Hailo-8L accelerator HAT pushes the Pi 5 to 13 TOPS and matches the Jetson for classic CV workloads. It will not run GGUF LLMs — it's a fixed-function NN accelerator programmed via Hailo's DFC toolchain. Think YOLO, pose estimation, semantic segmentation — not Llama.

Common deployment failure modes (and how to fix them)

1. Orin Nano Super throttling under 3 minutes of MAXN Super load. The reference cooler is just enough at 25 °C ambient. Above that, switch to an active Waveshare/Yahboom metal case with an extra 40 mm fan — it holds the SoC at 72 °C indefinitely.
2. CUDA OOM on Qwen/Llama 7B INT4. You forgot to stop GNOME. Switch to a minimal-image multi-user target or flash the Jetson minimal image without desktop — frees ~1.2 GB.
3. Ollama on Jetson reporting "no GPU" during inference. Use NVIDIA's dedicated Jetson-Ollama container, not the generic x86 image. The native Ollama installer script does not detect Jetson's Tegra GPU correctly.
4. Raspberry Pi 5 llama.cpp running at ~1 tok/s instead of the expected 5. You're using an older llama.cpp build without NEON optimizations or you're running on swap. Confirm the model fits in RAM before you load it.
5. Pi 5 "undervoltage detected" warning during AI load. The Pi 5 genuinely needs the 5 V / 5 A USB-C PSU (not 3 A). This is not optional with the Hailo-8L HAT attached.

Decision matrix: which one should you actually buy?

Get neither if you're training models (buy a real discrete GPU — see our Best GPU for local AI in 2026 guide), or if you need >20 FPS multi-stream video analytics (step up to the Jetson Orin NX 16GB or AGX Orin).

Bottom line

The Orin Nano Super is the first NVIDIA dev kit where the price-to-performance math is genuinely reasonable for hobbyists and small-deployment commercial work. At $249 it's the cheapest way to get CUDA 12, TensorRT, and 20+ tok/s on a 7B LLM — and that combination doesn't exist anywhere else in this bracket. The Raspberry Pi 5 remains the right answer for everything that isn't specifically AI-bound, and a surprisingly capable inference device once you accept the ≤3B parameter ceiling. Most serious edge-AI builders will end up owning both within a year: Pi 5 for I/O and always-on services, Orin for the model-serving layer.

FAQ

Is the Jetson Orin Nano Super worth $249 over the Raspberry Pi 5? For AI workloads, unambiguously yes — you're paying ~3× the Pi's price for roughly 4–9× the LLM inference speed and an entire CUDA/TensorRT software stack the Pi cannot access. For non-AI edge workloads (home automation, retro gaming, GPIO projects, NAS, Kubernetes clusters) the Pi 5 is the better buy at a third of the total system cost.

Can the Orin Nano Super actually run Llama 3.1 8B? Yes, at Q4_K_M quantization, with about ~15 tok/s expected throughput in MAXN Super mode. You will be using ~6.5 GB of the 8 GB unified memory, so close the desktop environment and don't try to multitask. For anything larger than 8B parameters, step up to the Orin NX 16GB or AGX Orin.

What's the real-world token-per-second difference for a 7B model? Published benchmarks put the Orin Nano Super at 21.75 tok/s on Qwen2.5-7B INT4 via MLC (source: NVIDIA Jetson AI Lab forum). The Raspberry Pi 5 runs Mistral 7B Q4 at 2.00 tok/s per It's FOSS testing — a ~10× gap. On 3B-class models the gap shrinks to ~4.5×; on 1–2B models it's roughly 5–7×.

Do I need the Hailo-8L AI HAT to do AI on a Pi 5? Only if you want real-time vision. For small LLMs (under 3B parameters) the CPU path via Ollama or llama.cpp is perfectly usable. For YOLO-class real-time detection or pose estimation, the Hailo-8L at +$70 is essentially mandatory — CPU YOLO on the Pi 5 runs at ~2–3 FPS, which is below the threshold for anything useful.

Which runtime is fastest on the Jetson Orin Nano Super in 2026? MLC-LLM consistently produces the best numbers on 7B-class models — the 21.75 tok/s Qwen2.5-7B result came from an MLC build. Ollama via NVIDIA's official Jetson container is within ~10 % on small models and dramatically easier to set up. llama.cpp with the CUDA backend is the flexibility pick when you want custom quants or grammar-constrained sampling.

Does the Jetson Orin Nano Super work for training, not just inference? Not meaningfully. 8 GB of unified memory and an Ampere GPU with 1,024 CUDA cores can handle small fine-tuning runs (QLoRA on 1–3B models) but the iteration speed is poor compared to a used RTX 3090 or 4090. Treat the Jetson as a deployment target and keep a separate workstation for training.

Sources

1. NVIDIA Jetson Orin Nano Super Developer Kit — official product page and spec sheet — MSRP, TOPS, power modes, module specs.
2. Cytron: DeepSeek-R1 on the Jetson Orin Nano Super — 24.20 tok/s Q4 benchmark, reproducible setup.
3. DEV Community (Ajeet Raina): DeepSeek-R1 + Docker + Ollama on Jetson — 30.98 tok/s optimized result.
4. NVIDIA Jetson AI Lab developer forums — Qwen2.5-7B MLC benchmark thread — 21.75 tok/s on INT4.
5. Jeff Geerling: Who would buy a Raspberry Pi 5 16GB? — Llama 2 13B at 1.50 tok/s and general Pi 5 LLM analysis.
6. Stratosphere Laboratory: How well do LLMs perform on a Raspberry Pi 5? — BitNet B1.58 2B at 8 tok/s.
7. Raspberry Pi Foundation: Official Pi 5 benchmarks — Geekbench 6 and CPU performance baselines.

Related guides

• Local AI on Raspberry Pi 5: Real Benchmarks for Llama, Phi, and Gemma
• Raspberry Pi 5 8GB Review (2026): Still the SBC to Beat
• Orange Pi 5 Plus vs Raspberry Pi 5: The Honest Head-to-Head
• Best Raspberry Pi Alternatives for SBC Enthusiasts in 2026
• Build an 8-Node Raspberry Pi Cluster for Distributed Computing

— SpecPicks Editorial · Last verified Apr 24, 2026