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Short answer: Raspberry Pi 5 can run local AI using its CPU, but an AI HAT is the better path when your model is supported by the Hailo accelerator. Choose AI HAT+ for supported vision workloads, and choose AI HAT+ 2 when local language or vision-language models are the goal. The published 13, 26 and 40 TOPS figures are accelerator specifications, not a tested CPU speed multiplier, so the right choice depends on your exact model, precision, runtime, memory and latency target.
What “local AI” means on a Raspberry Pi 5
Local inference means the Pi receives an input, runs the model and returns a result without sending that inference request to a cloud service. The input might be a camera frame, an image, an audio feature set, a sensor stream or a text prompt. On a Raspberry Pi 5, the model can run on the board’s CPU, on a Hailo neural-processing unit (NPU) attached through an AI HAT, or through a split pipeline in which the CPU manages the application while the NPU performs supported neural-network operations.
Raspberry Pi describes the distinction plainly: “The Hailo NPU is an AI accelerator chip designed to run neural networks; instead of Raspberry Pi device’s CPU doing the AI work, the NPU handles it more efficiently.” That sentence from the Raspberry Pi AI software documentation explains the architecture, not a universal benchmark. It does not mean every model will run faster on a HAT, nor that a TOPS number can be converted into a fixed CPU-versus-NPU multiplier.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteFor a small experiment, a CPU-only baseline is useful because it has no accelerator purchase and fewer compatibility decisions. For a production-style camera, robotics or concurrent-inference project, a supported Hailo path can free the CPU for image capture, control logic, networking and user-interface work. The deciding question is therefore not simply “Can the Pi run AI?” It is “Does the model and workload match the accelerator I am considering?”
#1 Best Overall
- Hailo-10H AI accelerator delivering 40 TOPS (INT4) inferencing performance.
- Performance for computer vision models comparable to the Raspbery Pi AI HAT+ (26 TOPS).
- Runs generative AI models efficiently using 8GB on-board RAM.
- Fully integrated into Raspbery Pi’s camera software stack.
- Conforms to Raspbery Pi HAT+ specification.
CPU, AI HAT+ and AI HAT+ 2 at a glance
| Option | Compute path | Documented workload fit | Published figure | Memory arrangement | Important boundary |
|---|---|---|---|---|---|
| Raspberry Pi 5 CPU | Pi 5 host CPU | The official materials reviewed do not provide a like-for-like performance result against an AI HAT. | No CPU accelerator figure is published in the cited materials. | Uses Raspberry Pi 5 system memory. | Model size, runtime, quantization and speed depend on the implementation. |
| AI HAT+ 13 TOPS | Hailo-8L NPU | Supported vision and neural workloads, including object detection, segmentation, pose estimation, camera post-processing and robotics. | 13 TOPS, INT8. | Uses Raspberry Pi 5 host memory. | Raspberry Pi does not list LLM support for this product. |
| AI HAT+ 26 TOPS | Hailo-8 NPU | The same supported vision and neural-workload category as the 13 TOPS variant, with a higher accelerator tier. | 26 TOPS, INT8. | Uses Raspberry Pi 5 host memory. | It is still a supported-model accelerator, not a general-purpose LLM device. |
| AI HAT+ 2 | Hailo-10H NPU | AI HAT+ workloads plus documented local large-language-model (LLM) and vision-language-model (VLM) use. | 40 TOPS, INT4. | 8 GB onboard memory in addition to host system memory. | Raspberry Pi positions it for models up to approximately 6 billion parameters; actual compatibility and speed depend on software and model support. |
These specifications come from Raspberry Pi’s AI HAT documentation. Because the AI HAT+ figures are INT8 and the AI HAT+ 2 figure is INT4, they are not directly interchangeable application-performance numbers.
What the TOPS numbers do—and do not—tell you
TOPS means trillions of operations per second under the accelerator’s stated arithmetic format. It is useful for identifying the relative product tier, but it is not an end-to-end result for your application. A camera pipeline also spends time capturing frames, resizing images, moving tensors, running post-processing and delivering results to the application. A language model adds tokenization, prompt processing, autoregressive generation and memory traffic. Software support can determine whether a layer runs on the NPU or falls back to the CPU.
Comparing 13 TOPS with 26 TOPS suggests different accelerator capacity within the AI HAT+ family; it does not prove that every model will run exactly twice as fast. Comparing either INT8 figure with AI HAT+ 2’s 40 TOPS INT4 figure is even less direct. Precision, model graph, operator coverage, batch size, input resolution, sequence length and thermal conditions all affect the result.
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No controlled test in the cited Raspberry Pi material runs the same model, quantization, software build, cooling setup and input on both the Pi 5 CPU and an AI HAT. Consequently, there is no defensible universal statement such as “the HAT is X times faster.” If you need a measured number, test your own model using the procedure later in this guide.
When CPU-only inference is the sensible choice
Early experiments and small models
Start with the CPU when you are still validating the application, trying several runtimes or using a model for which no Hailo-compatible path is documented. You can establish whether the input data, preprocessing and output quality are correct before introducing accelerator-specific conversion and deployment work. A CPU baseline also gives you a fallback mode for maintenance, diagnostics or installations where the HAT is unavailable.
Unsupported or rapidly changing model stacks
The HAT does not automatically accelerate every neural network. Raspberry Pi says automatic detection and offloading apply to supported AI workloads, and that the relevant software components and supported models are still required. If your framework, operators or model format are outside that support path, the CPU may be the only straightforward option.
Low-duty-cycle workloads
A script that analyzes one image occasionally may not justify an accelerator. The practical benefit of a HAT grows when inference is continuous, when several streams must be processed concurrently, or when the CPU must remain responsive for control and application tasks. Measure the complete response time, including startup and data movement, rather than looking only at model execution.
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What CPU-only does not guarantee
The available official material does not establish a CPU speed, a maximum model size or a quality comparison for Pi 5 inference. Treat CPU operation as a compatibility and baseline choice, not as a promised performance level.
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
What AI HAT+ is designed to accelerate
Vision and neural-network pipelines
Raspberry Pi lists object detection, image segmentation, pose estimation, camera post-processing and robotics among the supported workload categories for AI HAT+. These are the kinds of pipelines where a detector or other neural network runs repeatedly while the Pi handles camera I/O, business logic, actuation and communications.
Choosing between 13 TOPS and 26 TOPS
The 13 TOPS variant uses Hailo-8L and the 26 TOPS variant uses Hailo-8; both figures are INT8. The higher tier is relevant when your supported model needs more throughput, when you have multiple camera streams or when concurrency is a requirement. It is not automatically the right choice for a model that is unsupported on the Hailo software stack.
Raspberry Pi’s product information lists the AI HAT+ from a $70 starting price, while the product brief lists $70 for the 13 TOPS version and $110 for the 26 TOPS version. These are vendor list-price references shown in material accessed on 2026-10-13, not a universal current retail quote; check the official product page for your region and date.
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The AI HAT+ documentation specifically says LLMs are not supported on AI HAT+. If your defining requirement is a local text-generation assistant, a multimodal chat model or another generative model, do not buy the standard HAT on the assumption that its TOPS figure implies LLM support. Evaluate AI HAT+ 2 or retain a CPU-only plan that matches your model and runtime.
Why AI HAT+ 2 is the generative-AI choice
AI HAT+ 2 uses a Hailo-10H NPU rated at 40 TOPS in INT4 and includes 8 GB of onboard memory. Raspberry Pi documents it for local LLM and VLM workloads as well as the supported vision workloads associated with the AI HAT family. The company describes a model scale of up to approximately 6 billion parameters. That is a positioning statement, not a guarantee that every six-billion-parameter model, quantization or prompt length will fit or run at a useful rate.
The dedicated memory changes the design calculation. AI HAT+ uses Raspberry Pi 5 memory, while AI HAT+ 2 adds its own 8 GB. For generative workloads, memory capacity and bandwidth can be as important as arithmetic throughput because weights, key-value caches and intermediate tensors must remain available during generation.
Raspberry Pi announced AI HAT+ 2 for generative AI on 2026-01-15 in its official announcement. Before committing to a model, verify that the current Hailo GenAI software stack supports its architecture, quantization and context requirements. “Up to approximately 6B” should guide your search, not replace that compatibility check.
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- Board: Raspberry Pi’s documented Hailo setup requires a Raspberry Pi 5.
- Operating system: Use a 64-bit Raspberry Pi OS installation for the documented software path.
- Software: Install the Raspberry Pi AI software components and the supported model/runtime integration for your workload. Detection of the accelerator does not remove the need for those components.
- Model: Confirm that the exact network, operators, input shape, precision and task are supported. “AI workload” is not a sufficient compatibility description.
- Camera projects: Use a supported camera for the documented vision path; Raspberry Pi names Camera Module 3 as an example.
- Application design: Decide whether the CPU will perform preprocessing, post-processing, orchestration and control while the NPU performs the supported neural graph.
The AI software documentation and AI HAT documentation are the authoritative places to check current software and model support because those details can change independently of the hardware specification.
Rank #3
- ⚡ PoE HAT for Raspberry Pi 5 CM5: PoE HAT F is a Power over Ethernet expansion board for Raspberry Pi 5 and CM5, supporting network connection and power input through one Ethernet cable.
- 🔌 802.3af/at PoE+ Support: This PoE+ HAT supports IEEE 802.3af/at network standard and works with compatible PoE power sourcing equipment for compact wired deployment projects.
- 🧊 Active Cooling Fan and Metal Heatsink: The PoE HAT with cooling fan includes a metal heatsink and high-speed active fan, helping improve heat dissipation and operating stability during long-term use.
- 🔋 5V and 12V Output Headers: Onboard 5V and 12V header outputs provide power options for external peripherals, with up to 25W total output under suitable PoE input and cooling conditions.
- 🧩 40-pin GPIO Stackable Header: Standard 40-pin GPIO stackable header fits Raspberry Pi 5 and CM5 expansion, allowing users to connect compatible HATs and custom project interfaces.
Installation and first-run workflow
The exact package names and commands can change with Raspberry Pi OS and Hailo software releases, so follow the current commands in Raspberry Pi’s documentation rather than copying an old tutorial. The hardware and verification sequence is stable:
- Prepare the host: Install a current 64-bit Raspberry Pi OS image on the Raspberry Pi 5, boot it and apply the normal operating-system updates.
- Power down before fitting hardware: Shut down the Pi, disconnect power and attach the AI HAT+ or AI HAT+ 2 using the supplied mechanical and board connections. Follow the HAT’s assembly instructions; do not attach or remove it while powered.
- Boot and install the documented AI components: Use the installation path in Raspberry Pi’s AI software documentation for your HAT generation and intended workload.
- Verify detection: Confirm that Raspberry Pi OS detects the accelerator before debugging a model. A detection failure is a host, connection or software problem, not a model-conversion problem.
- Validate with a supported example: Run a documented supported vision or generative-AI example first. This separates a known-good software installation from issues in your own model.
- Add your application: Introduce your camera, sensor, prompt or robotics control loop only after the example produces the expected output.
- Measure the complete path: Record input-to-result latency, sustained throughput, CPU utilization, memory use and thermal behavior under the workload you actually plan to deploy.
Camera and robotics projects have extra moving parts
In a camera application, the neural network is only one stage. The system must capture frames, negotiate formats, resize or normalize data, submit the supported graph, decode detections or keypoints and then act on them. Raspberry Pi’s camera frameworks can use the Hailo NPU for supported tasks such as image recognition and object detection, but the documented camera examples require a supported camera. Camera Module 3 is one example named by Raspberry Pi.
For robotics, separate the control loop from inference timing. A detector that occasionally takes longer than expected can affect actuation, even if its average throughput looks acceptable. Keep safety-critical behavior independent of an unbounded inference call, and record worst-case latency rather than only the average. The HAT can perform supported neural work; it does not replace the Pi’s responsibility for scheduling, sensor handling or safe motor control.
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How to benchmark CPU versus HAT fairly
If a purchase depends on measured performance, run both paths under controlled conditions. The result should be a statement about your model and build, not a generic claim about all Raspberry Pi AI workloads.
- Freeze the model: Use the same architecture, weights, input resolution, batch size and quantization wherever both paths support them.
- Freeze the input: Use the same images, video clip, sensor sequence or prompts. For an LLM, record prompt length, generated-token limit and sampling settings.
- Freeze the software context: Record Raspberry Pi OS version, runtime versions, model-conversion settings and Hailo software version.
- Separate warm-up from steady state: Report initialization time separately, then measure a defined number of warmed-up inferences or generated tokens.
- Measure useful metrics: Report median and tail latency, frames or tokens per second, CPU utilization, memory use and dropped inputs. For a camera, include capture and post-processing if that is what the user experiences.
- Control thermals: Use the same enclosure, ambient conditions and cooling arrangement for comparable runs. Note any throttling or frequency changes.
- Check output equivalence: Verify that CPU and NPU results meet the same accuracy or response-quality requirement. A faster result that changes detections or generated output may not be an acceptable substitute.
This process is necessary because the official product pages publish accelerator specifications and supported use cases, not a controlled CPU-versus-HAT benchmark.
Common failure modes and fixes
The Pi boots but the accelerator is not detected
- Power down and reseat the HAT and its board connection.
- Confirm that the board is a Raspberry Pi 5 and that the operating system is 64-bit Raspberry Pi OS.
- Recheck the current Raspberry Pi AI software installation steps and verify detection before attempting model deployment.
The model runs, but inference stays on the CPU
- Check whether the exact model and operators are supported by the Hailo path.
- Confirm that the required software component is installed and that the application is using the accelerator-enabled runtime.
- Look for unsupported layers or preprocessing that force a fallback. A detected HAT does not mean every operation in a graph is offloaded.
A camera example fails before inference
- Verify that the camera is one of the supported devices; Camera Module 3 is the example named in Raspberry Pi’s documentation.
- Test camera capture independently, then test the documented AI example before substituting your own model.
- Check frame format, resolution and permissions in the camera software path.
An LLM does not fit or performs poorly on AI HAT+ 2
- Check architecture, quantization, context length and current Hailo GenAI support rather than relying only on the approximate six-billion-parameter positioning.
- Reduce context or generation requirements for a diagnostic run, then measure whether the resulting quality and latency meet your application’s needs.
- Remember that AI HAT+ 2’s 40 TOPS figure is INT4; it should not be compared directly with the INT8 figures of AI HAT+.
The application is slower than the model benchmark
- Measure capture, preprocessing, transfer, post-processing and output handling separately.
- Check for CPU contention from networking, logging, user-interface work or multiple streams.
- Measure sustained operation after warm-up and under the final enclosure and cooling conditions.
Legacy AI Kit versus current HAT products
The Raspberry Pi AI Kit is no longer in production. Raspberry Pi describes it as functionally similar to the Hailo-8L/13 TOPS option and recommends AI HAT+ or AI HAT+ 2 for new designs. That makes the AI Kit a compatibility consideration only for an existing installation or a specific legacy replacement; it is not the forward-looking purchase for a new Pi 5 project. See the official AI Kit page for Raspberry Pi’s lifecycle statement.
Availability and lifecycle considerations
Raspberry Pi’s AI HAT+ product brief and product page state production through at least January 2030. That horizon can matter for an installed camera or robotics fleet that needs a consistent hardware supply. It does not guarantee that every software version, model or retailer will remain unchanged, so record the supported software stack used in your deployment and monitor Raspberry Pi’s documentation for updates.
No current universal retail price for AI HAT+ 2 is established by the cited material. For AI HAT+, use the $70 starting-price context and the $70/$110 13 TOPS and 26 TOPS list-price references only as dated vendor guidance, then verify the current regional price before budgeting.
A practical decision framework
Choose CPU-only first when
- You are validating an idea, testing data quality or trying a model without documented Hailo support.
- Inference is occasional and CPU load does not interfere with the rest of the application.
- You need the fewest hardware and software dependencies while establishing a baseline.
Choose AI HAT+ when
- Your target is supported vision or neural inference such as detection, segmentation, pose estimation, camera processing or robotics.
- You need sustained throughput or concurrency beyond what a CPU-only design can comfortably provide, and your model is supported.
- You want the 13 TOPS or 26 TOPS tier, selected according to measured throughput and concurrency needs rather than the number alone.
Choose AI HAT+ 2 when
- Local LLM or VLM inference is a core requirement.
- The model fits the documented Hailo GenAI support path and benefits from the HAT’s 8 GB onboard memory.
- You also need the supported vision workloads available across the AI HAT family.
Keep a CPU fallback when
- The application must remain diagnosable if the accelerator, model package or HAT connection is unavailable.
- You expect to change models frequently and cannot guarantee accelerator support for every future version.
- Your deployment can tolerate lower throughput during maintenance or degraded operation.
Bottom line for Raspberry Pi 5 local AI
Raspberry Pi 5 is capable of local inference without an AI HAT, but the official material does not provide a numerical CPU-versus-HAT speed ratio. AI HAT+ is the targeted upgrade for supported vision and neural-network workloads: select 13 TOPS or 26 TOPS according to your model and concurrency measurements. AI HAT+ 2 is the documented choice for local LLM and VLM work, combining a 40 TOPS INT4 Hailo-10H accelerator with 8 GB of onboard memory and an approximate six-billion-parameter positioning. In every case, verify model and runtime support first, then benchmark the complete application path on the hardware you intend to deploy.
Quick Recap
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