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The practical way to speed up AI inference on a Raspberry Pi 5 is to add a Raspberry Pi AI HAT+ with a Hailo NPU and run a model supported by the Hailo software stack. Choose the 13 TOPS Hailo-8L board for moderate vision workloads such as detection, segmentation, and pose estimation; choose the 26 TOPS Hailo-8 board for larger networks or several models at once. If you need local large-language-model (LLM) or vision-language-model (VLM) inference, use AI HAT+ 2, which combines a 40 TOPS Hailo-10H NPU with 8 GB of onboard memory. Correct software, PCIe configuration, cooling, power, and model conversion matter as much as the TOPS rating, so no single frames-per-second figure applies to every project.
Which Raspberry Pi 5 accelerator should you use?
For most Raspberry Pi 5 camera projects, the current Raspberry Pi AI HAT+ is the appropriate accelerator. It is available with either a 13 TOPS Hailo-8L NPU or a 26 TOPS Hailo-8 NPU. AI HAT+ 2 is a different class of device: its 40 TOPS Hailo-10H NPU and dedicated memory are intended to support local LLM and VLM workloads as well as vision inference.
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The Tool Desk
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|---|---|---|---|---|
| Raspberry Pi AI HAT+ 13 TOPS | Hailo-8L, 13 TOPS (Raspberry Pi Ltd product documentation, 2026) | Uses the Raspberry Pi 5’s memory | Moderate object detection, segmentation, pose and similar vision models | Current product |
| Raspberry Pi AI HAT+ 26 TOPS | Hailo-8, 26 TOPS (Raspberry Pi Ltd product documentation, 2026) | Uses the Raspberry Pi 5’s memory | Larger vision networks or more simultaneous models | Current product |
| Raspberry Pi AI HAT+ 2 | Hailo-10H, 40 TOPS (Raspberry Pi Ltd documentation, 2026) | 8 GB onboard memory | Local LLM, VLM and demanding vision workloads | Current product |
| Raspberry Pi AI Kit | Hailo-8L, 13 TOPS (Raspberry Pi Ltd reference documentation, 2026) | M.2 accelerator bundle using the Pi’s memory | Existing-stock replacement for the earlier 13 TOPS configuration | No longer in production |
These TOPS figures describe the accelerator’s rated compute capability, not a guaranteed camera frame rate. Actual throughput depends on the converted model, input resolution, camera pipeline, number of streams, thermal condition, power delivery and software versions.
What is the difference between AI HAT+, AI HAT+ 2 and the AI Kit?
The key differences are supported model class, compute tier, memory and product lifecycle. AI HAT+ boards target Hailo-supported vision inference, while AI HAT+ 2 adds the hardware and memory intended for local language and vision-language models.
#1 Best Overall
- ✅Powered by 26 Tera-Operations Per Second (TOPS) Hailo-8 AI Processor. 2.5W typical power consumption
- ✅Scalable, enabling simultaneous processing of multi-streams & multi-models
- ✅Enabling real-time, low latency and high-efficiency AI inferencing on the edge devices
- ✅Supports TensorFlow, TensorFlow Lite, ONNX, Keras, Pytorch frameworks
- ✅Supports Linux and Windows. Supports the temperature range of -40°C to 85°C
AI HAT+ 13 TOPS
The 13 TOPS version is the sensible starting point when your workload is one or a few moderate camera models. It is suited to common edge-vision tasks and is the lowest-throughput current AI HAT+ option. Choose it when the model fits the Hailo stack and you do not need local LLM or VLM execution.
AI HAT+ 26 TOPS
The 26 TOPS version provides twice the published TOPS rating of the 13 TOPS model. That extra headroom is useful for a larger neural network, higher input resolution, parallel models or more demanding post-processing. It still uses the Pi 5 for system memory, so it is not a substitute for the dedicated memory in AI HAT+ 2 when a language model is the requirement.
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AI HAT+ 2
AI HAT+ 2 is the option to evaluate when the application must run an LLM or VLM locally rather than only a camera-vision model. Its 40 TOPS Hailo-10H NPU and 8 GB of onboard memory separate it from the two standard AI HAT+ variants. Confirm that the exact LLM or VLM and its runtime are supported before buying; a higher TOPS number does not make every model compatible.
AI Kit
The AI Kit pairs a 13 TOPS Hailo-8L accelerator on an M.2 board with the Raspberry Pi 5. Raspberry Pi says it is no longer in production, so it makes sense only when you already own one or find verified existing stock. Check the board’s condition, included thermal hardware and current software support before relying on it for a new deployment.
How do you choose an accelerator for a Raspberry Pi 5 project?
Start with the model class, then size compute and memory. Selecting by TOPS alone can leave you with a fast accelerator that cannot run the graph you need.
1. Identify the model class
- For object detection, segmentation, pose estimation and similar camera tasks, begin with AI HAT+ 13 TOPS.
- For a larger network, several camera models or parallel inference, consider AI HAT+ 26 TOPS.
- For local LLM or VLM inference, evaluate AI HAT+ 2 and its 8 GB onboard memory.
2. Check support before measuring performance
The Hailo software stack supports a defined set of models and frameworks. A model that runs on the Pi’s CPU or GPU may need conversion, quantization or graph changes before it can execute on the NPU, and some operators may not be available. Verify the exact model, input shape and runtime path in the current Hailo documentation before purchasing hardware or redesigning the application.
3. Estimate concurrency and resolution
Count the number of models and camera streams that must run at the same time. A single low-resolution detector may fit comfortably on 13 TOPS, while multiple high-resolution pipelines can justify 26 TOPS. Treat this as capacity planning rather than a promise of a particular frame rate.
4. Decide whether dedicated memory is required
AI HAT+ 13 TOPS and 26 TOPS use the Raspberry Pi 5’s memory. AI HAT+ 2 has 8 GB on the accelerator, which is relevant when an LLM or VLM’s working set would otherwise compete with the operating system and application on the Pi.
How do you install an AI HAT+ on Raspberry Pi 5?
Use a 64-bit Raspberry Pi OS installation, the documented AI dependencies and a model supported by the Hailo stack. The physical installation is only one part of the process; software and thermal setup determine whether the NPU is actually used.
Rank #2
- HIGH PERFORMANCE: Features 26 TOPS (Trillion Operations Per Second) AI acceleration capability through the Hailo AI Accelerator for advanced machine learning applications
- COMPATIBILITY: Specifically designed for the Raspberry Pi 5, connecting via PCIe interface for optimal data transfer and processing speeds
- COMPACT DESIGN: Measures 65mm x 56.5mm, offering a space-efficient solution while maintaining full functionality as an AI acceleration add-on board
- TEMPERATURE RANGE: Operates reliably in temperatures from 0°C to +50°C (32°F to 122°F), ensuring stable performance in various environments
- SEAMLESS INTEGRATION: Functions as a HAT (Hardware Attached on Top) add-on board, providing plug-and-play compatibility with Raspberry Pi ecosystem
- Prepare the operating system. Start with a current 64-bit Raspberry Pi OS installation on a Raspberry Pi 5. Bring the system up to date and install the AI packages specified by the current Raspberry Pi and Hailo documentation. A 32-bit installation or an outdated image can prevent the supported runtime from loading.
- Shut down and attach the board. Power off the Pi, fit the AI HAT+ or AI HAT+ 2 through the board’s supported hardware interface, and install the supplied mechanical and thermal hardware as directed. Do not work on the connector while the Pi is powered.
- Configure the interface. AI HAT boards apply the required PCIe setting automatically. If you are using the older AI Kit, manually enable PCIe Gen 3.0 as described in its setup documentation; omitting this setting can limit or prevent accelerator operation.
- Install the supported model software. Add the documented Hailo runtime, conversion tools and model packages, then select a model that the installed software version supports. Keep the model’s input dimensions and preprocessing settings aligned with the camera or image source.
- Connect cooling and power. Install active cooling, keep vents clear and place the thermal pad correctly so heat transfers from the board. For the setup described in Hailo’s guide, use the official Active Cooler and a 27 W USB-C power supply. A marginal supply or enclosed case can cause throttling and make a working model appear unreliable.
- Run a supported reference pipeline. For camera inference, use a supported
rpicam-appsorPicamera2pipeline. These paths allow the camera post-processing stages to be offloaded to the Hailo NPU instead of leaving all inference work on the Pi’s CPU. - Validate acceleration. Confirm that the application reports the Hailo device and that inference is not silently falling back to the CPU. Test with a known supported model before introducing custom preprocessing, unsupported operators or multiple streams.
How should you configure camera inference?
Use Raspberry Pi’s supported camera applications rather than treating the accelerator as a generic USB inference device. The documented rpicam-apps and Picamera2 integrations can place compatible post-processing on the Hailo NPU.
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Choose the pipeline that matches your application: rpicam-apps is appropriate for command-line and camera-application workflows, while Picamera2 is appropriate when Python controls capture and application logic. Install the versions and AI post-processing components documented for your Raspberry Pi OS release.
Keep preprocessing consistent
Inference speed and accuracy can change when you alter image size, color format, cropping or frame rate. Match the model’s expected input to the pipeline’s preprocessing configuration and avoid unnecessary copies between camera buffers and the inference process.
Scale concurrency deliberately
Run one model first, then add streams or post-processing one at a time. This shows whether the 13 TOPS board has enough headroom or whether the 26 TOPS version is justified. The NPU can remove the main inference bottleneck while decoding, resizing, application logic or display still consume Pi CPU time.
How much faster will Raspberry Pi 5 inference become?
There is no universal speed-up percentage or frame-rate guarantee. The published material identifies the accelerator’s 13, 26 and 40 TOPS ratings but does not establish one benchmark covering every model, resolution and camera pipeline.
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Measure your own workload in two stages:
- Record a CPU-only baseline. Measure end-to-end latency and sustained throughput with the exact input resolution, preprocessing and number of streams you plan to deploy.
- Measure the accelerated path. Use the same input and application behavior after the Hailo pipeline is active. Record warm-up time, steady-state latency, dropped frames and CPU utilization, not only the instantaneous inference time.
- Repeat under heat and power conditions. Test after the system reaches operating temperature and while the enclosure, cooler and USB-C supply are configured as they will be in production.
- Check accuracy as well as speed. Conversion or quantization can change model output. Compare detections, masks or poses with the original implementation before treating a faster result as acceptable.
These measurements separate NPU compute from bottlenecks elsewhere in the application. A camera driver, image conversion, storage operation or display loop can dominate total latency even when the neural-network stage is accelerated.
Can Raspberry Pi 5 run local LLMs or VLMs with an accelerator?
AI HAT+ 2 is the Raspberry Pi option specifically intended for local LLM and VLM use because it combines a 40 TOPS Hailo-10H NPU with 8 GB of onboard memory. The standard AI HAT+ 13 TOPS and 26 TOPS boards are primarily vision accelerators and should not be selected merely because their TOPS figures look adequate for a language model.
Before deployment, verify the model’s supported precision, context size, operators and runtime integration. A model may require conversion or a supported variant, and the Pi’s CPU, storage and application memory still affect startup and response time. Treat the 8 GB figure as accelerator memory, not as an upgrade to the Raspberry Pi 5’s system RAM.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.The Hailo NPU allows Raspberry Pi 5 to run hardware-accelerated AI models locally, removing the need to send data to a remote cloud server for processing. — Raspberry Pi documentation, AI HATs page
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Hailo-8 M.2 AI Accelerator Module 26TOPS Hailo8 Support Linux/Windows
- Powered by 26 Tera-Operations Per Second (TOPS) Hailo-8 AI Processor.
- 2.5W typical power consumption
- Enabling real-time low latency and high-efficiency AI inferencing on the edge devices
- Supports TensorFlow TensorFlow Lite, ONNX, Keras, Pytorch frameworks
- Supports Linux and Windows.
What can prevent the accelerator from working or reaching full speed?
Most failures come from software compatibility, PCIe configuration, thermal limits or power delivery rather than a defective NPU.
| Symptom | Likely cause | What to check |
|---|---|---|
| The application cannot find a Hailo device | Missing runtime, incorrect board connection or an unsupported OS/software combination | Recheck the 64-bit Raspberry Pi OS requirement, installed dependencies, physical connector and device detection reported by the supported pipeline. |
| An AI Kit works inconsistently or not at all | PCIe Gen 3.0 was not enabled | Apply the manual PCIe Gen 3.0 setting required by the AI Kit documentation, then reboot and test again. |
| Inference falls back to the CPU | The model or an operator is not supported, or the Hailo post-processing path is not active | Test a documented reference model and inspect the pipeline’s accelerator status before modifying the custom graph. |
| Performance declines during a long run | Thermal throttling, inadequate airflow or insufficient power | Fit the thermal pad correctly, use active cooling, ventilate the enclosure and provide the power level specified for the setup. |
| Accuracy changes after conversion | Different preprocessing, quantization or unsupported graph substitutions | Compare input preprocessing and outputs with the original model, then use a supported model variant or revise the conversion. |
| One stream is fast but several streams stutter | Insufficient NPU headroom or a CPU-side bottleneck | Profile each stage, lower input resolution if acceptable, simplify post-processing or move to the 26 TOPS board for additional compute capacity. |
Is the Raspberry Pi AI Kit still worth buying?
Only in limited circumstances. Raspberry Pi lists the AI Kit as no longer in production, although its 13 TOPS Hailo-8L capability is functionally equivalent to the 13 TOPS AI HAT+ tier.
Existing owners can continue using it if the board, cable or mounting hardware is in good condition and the current software stack supports the intended model. A new project should normally use the current AI HAT+ instead, because availability, documentation and lifecycle are easier to verify. For used hardware, check for damaged connectors, missing thermal parts, uncertain provenance and the ability to enable PCIe Gen 3.0 before committing to a deployment.
Which setup is right for common projects?
Use this decision checklist to avoid paying for capacity your model cannot use:
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- Single moderate camera model: AI HAT+ 13 TOPS, provided the model is supported and the measured workload fits.
- Higher resolution, larger network or multiple vision models: AI HAT+ 26 TOPS, followed by an end-to-end concurrency test.
- Local LLM or VLM: AI HAT+ 2, after confirming model and runtime support.
- Already own an AI Kit: Keep it for compatible 13 TOPS vision work if it passes hardware and software checks; do not treat it as a current product for a new build.
Whichever board you choose, install the current 64-bit operating system and documented dependencies, use a supported camera pipeline, provide active cooling and adequate USB-C power, and benchmark the complete application rather than relying on the TOPS label alone.
Frequently Asked Questions
Does the Raspberry Pi 5 need an accelerator for every AI model?
No. Small or infrequent models can run on the Pi alone, although CPU inference may leave less capacity for camera capture and application logic. An accelerator becomes useful when sustained latency, multiple streams or local model execution exceeds what the CPU can handle.
Can I use a custom TensorFlow or PyTorch model on an AI HAT+?
Only if the model can be converted to a graph and operators supported by the installed Hailo software stack. Plan for a compatibility check and output-validation step; framework support by itself does not guarantee NPU execution.
Will AI HAT+ 26 TOPS run twice as many cameras as the 13 TOPS board?
Not necessarily. The published rating is a compute capacity figure, while camera throughput also depends on resolution, preprocessing, post-processing and CPU overhead. Benchmark the exact number of streams and model combination you intend to deploy.
What memory does AI HAT+ 2 add to my Raspberry Pi 5?
AI HAT+ 2 has 8 GB of onboard memory for its accelerator workloads. That memory is separate from the Raspberry Pi 5’s system RAM and does not increase the Pi’s general-purpose memory pool.
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