A bottom-mount baseboard for Raspberry Pi 5 that combines the DEEPX DX-M1 M.2 AI Module (up to 25 TOPS at INT8), an NVMe SSD slot, and an LTE/5G modem slot in one stack. The AI accelerator connects over PCIe Gen 2/Gen 3 x1 via a 40 mm FFC; storage and cellular share an internal USB 3.0 (5 Gbps) hub. One USB-C PD input powers the full stack and back-powers the Pi 5 through pogo pins. Intelligented by DEEPX. Built on Raspberry Pi.
Up to 25 TOPSDEEPX DX-M1Triple M.2Raspberry Pi 5PCIe Gen 2/Gen 3 x1USB-C PD
Edge AI Expansion Board·Overview·Intelligented by DEEPX·Built on Raspberry Pi
NPU
Up to 25 TOPS
INT8 · DEEPX DX-M1 M.2 AI Module
Host link
PCIe Gen 2/Gen 3
x1 · 40 mm PCIe FFC
Expansion
3× M.2
AI + NVMe + LTE/5G
Power input
USB-C PD
27 W min · 45 W recommended
What the Edge AI Expansion Board is
The Sixfab Edge AI Expansion Board is an under-board baseboard for Raspberry Pi 5 that integrates three subsystems production vision workloads usually need together: a dedicated DEEPX neural processing unit for inference, fast local storage for buffering and logging, and a cellular modem for wide-area connectivity. All three slots can be populated and operated concurrently with no measurable degradation in AI throughput.
The AI accelerator slot connects to the Raspberry Pi 5 over a dedicated PCIe Gen 2/Gen 3 x1 link through a 40 mm FFC cable, separate from the NVMe and cellular paths. PCIe Gen 2 is the default on Raspberry Pi OS for stability; Gen 3 is enabled by adding dtparam=pciex1_gen=3 to /boot/firmware/config.txt. Measured bandwidth is 400–450 MB/s at Gen 2 and 800–900 MB/s at Gen 3.
The NVMe and cellular slots are routed through an on-board USB 3.0 (5 Gbps) hub: NVMe via a Realtek RTL9210B-CG USB-to-NVMe bridge, cellular via the USB Bridge PCBA. The OS sees these as standard USB Mass Storage and USB cellular devices, so they require no extra kernel drivers beyond what Raspberry Pi OS ships. The board is shipped today with a vision focus: object detection, classification, segmentation, pose estimation. LLMs are on the DEEPX silicon roadmap and Sixfab will support them as the silicon enables. No dates.
Two variants
The Edge AI Expansion Board ships in two bundle compositions. Both contain the same baseboard, the same mounting kit, and the same accessories. The difference is whether the DEEPX DX-M1 M.2 AI module is included in the box.
Variant 1: Board only
DEEPX M1 ready
Module not included
AI Expansion Board1×
USB 3.0 Bridge1×
PCIe Cable (40 mm FFC)1×
M2.5 15 mm F-F plastic spacer6×
M2.5 5 mm M-F plastic spacer4×
M2.5 screw6×
M2 flathead screw3×
M2 module plastic spacer3×
USB Type-C plastic cap1×
DEEPX DX-M1 M.2 AI moduleNot included
For customers who already have a DEEPX DX-M1 M.2 AI Module, want to source the module on a separate purchase order, or need to standardise a single board SKU across deployments.
Variant 2: Board + DEEPX M1 module
Ready to inference out of the box
Bundled module
AI Expansion Board1×
USB 3.0 Bridge1×
PCIe Cable (40 mm FFC)1×
M2.5 15 mm F-F plastic spacer6×
M2.5 5 mm M-F plastic spacer4×
M2.5 screw6×
M2 flathead screw3×
M2 module plastic spacer3×
USB Type-C plastic cap1×
DEEPX DX-M1 M.2 AI module1× included
The single-SKU path from box to first inference. Includes the DEEPX DX-M1 M.2 module pre-bundled with the board; install, stack on the Pi 5, install the runtime, and run dxrt-cli -s.
Quantization and accuracy
The DEEPX silicon runs INT8 only. The DXNN compiler quantizes models from FP32 to INT8 automatically; expect approximately 2 % accuracy loss compared with the original trained model. Plan benchmarks and acceptance criteria around the quantized model, not the FP32 baseline.
Key features
DEEPX NPU on a swappable M.2 module
The DEEPX DX-M1 M.2 AI Module delivers up to 25 TOPS at INT8 precision on a removable M.2 M-Key module rather than soldered to the PCB. Swap or replace the module without changing the board, with the Pi 5 powered off.
Triple M.2: AI + NVMe + LTE/5G
Three dedicated slots: M.2 M-Key for the DX-M1 over PCIe, M.2 M-Key for NVMe storage via the on-board USB 3.0 hub, and M.2 Key-B for an LTE/5G modem with a nano SIM slot (eUICC supported).
Single USB-C PD powers the stack
One USB-C PD input (5 V/5 A · 9 V/3 A · 12 V/2.25 A) feeds the board and back-powers Raspberry Pi 5 through pogo pins on the GPIO header underside. The Pi 5's own USB-C port stays free and gets a blanking plug.
Two software paths
Start with the Sixfab Model Zoo for pre-compiled DXNN models, or use the DXNN SDK for the full ONNX → DXNN compile flow. The Sixfab × Ultralytics acceleration path takes a labeled dataset to a deployed custom model in days.
On-device inference, no cloud
After the runtime install, the system runs fully offline. No frames or model outputs leave the Pi 5 unless the user application explicitly forwards them, a fit for privacy-sensitive vision deployments. Cellular is for outbound results, not inference.
Concurrent AI + storage + cellular
The dedicated PCIe lane keeps the NPU isolated from the USB hub. Simultaneous NVMe writes and LTE/5G traffic do not degrade AI throughput. Buffer locally, forward over WAN, and infer, all at the same time.
How a deployment runs on the stack
The Edge AI Expansion Board lets the Raspberry Pi 5 keep doing what it is good at (capture, application logic, and I/O) while offloading inference to the DEEPX NPU and pushing buffered data through storage or the cellular link.
Deployment data flow
Step 1
Capture on Pi 5
CSI, USB, or IP camera feeds frames into the Pi 5. The Pi handles capture and pre-processing.
Step 2
NPU inference
DEEPX NPU runs the compiled DXNN model. INT8 execution over PCIe Gen 2/Gen 3 x1.
Step 3
Buffer on NVMe
Frames, detections, or events written to NVMe via the on-board USB 3.0 hub. 380–440 MB/s sequential.
Step 4
Forward over LTE/5G
Events or summaries pushed out through the M.2 Key-B modem. Raw frames never need to leave.
Software: two paths to a running model
Both paths run on top of the same DEEPX runtime (dxrt-runtime) and the kernel driver shipped from the Sixfab APT repository on Raspberry Pi OS. The difference is what is on disk before dxrt-cli -s is run.
Validate the hardware first with the Sixfab Model Zoo: a Pi 5, an Edge AI Expansion Board with the DX-M1 installed, and a YOLOv8n DXNN file are enough to confirm the NPU is detected on PCIe and producing correct output. From there, move to the DXNN SDK when the application needs a model that is not in the zoo or a custom-trained one.
What you can build
The Edge AI Expansion Board is sized for connected edge deployments where local inference, on-device buffering, and dependable WAN connectivity are required at the same time. Examples below; this is a sample, not a limit.
Connected AI cameras & security
Detection and intrusion analytics with on-device buffering to NVMe and event forwarding over LTE/5G. Frames stay local, only events leave.
Smart-city & infrastructure
Traffic and pedestrian analytics with cellular backhaul. Suitable for sites without wired networking; power the stack from a single USB-C PD supply.
Mobile & stationary robotics
AMRs and inspection robots that need vision inference plus connected logging: NVMe for telemetry, LTE/5G for OTA fleet links.
Industrial inspection & QC
Defect detection on assembly lines with local storage of evidence frames and remote dashboards via cellular when wired networking is restricted.
Remote monitoring & AIoT gateways
Sensor fusion plus on-device inference at unattended sites. Pogo-pin back-power keeps the wiring on one connector for easier enclosure design.
Privacy-sensitive analytics
Healthcare, retail, and workplace deployments where raw video must not leave the device. Inference runs locally; only structured outputs travel.
What the Edge AI Expansion Board does not do
Stating limits up front is part of the brand contract. Plan deployments against the lines below; do not plan against marketing speculation.
Vision today, LLMs later
The DEEPX silicon is sized for vision and similar inference workloads. LLM support is on the DEEPX roadmap and Sixfab will support it as the silicon enables. No dates.
No on-device training
Training is done independently on a host machine. Trained models are exported to ONNX and compiled with the DXNN compiler before deployment, or built through the Sixfab × Ultralytics acceleration path.
Not hot-pluggable
Hot-plug is not supported. The Raspberry Pi 5 must be powered off and the USB-C cable disconnected before the Edge AI Expansion Board or any M.2 module is mounted or removed. Inserting or removing a module while the stack is powered can damage the hardware.
INT8 only on the NPU
The DXNN compiler quantizes from FP32 to INT8 automatically. Approximately 2 % accuracy loss versus the original trained model is expected.
NVMe is USB-attached
The NVMe slot is not a native PCIe device. It is routed through the on-board USB 3.0 (5 Gbps) hub and a Realtek RTL9210B-CG bridge, so the OS sees a USB Mass Storage device (/dev/sda) capped at the 5 Gbps USB bus — 380–440 MB/s sequential read in practice, not full PCIe NVMe speed.
Needs the 27 W PSU
It does not run on standard 5 V / 3 A (15 W) supplies. The official Raspberry Pi 27 W USB-C PD power supply is the minimum; 45 W is recommended for full-stack configurations (AI + NVMe + cellular populated).
Compatibility
The Edge AI Expansion Board is designed for the Raspberry Pi 5 host platform. The mechanical footprint, the pogo-pin back-power layout, the PCIe FFC orientation, and the USB hub routing are tuned to the Pi 5 board revision.
Supported host platforms
Supported
Raspberry Pi 5 with 4 GB, 8 GB (recommended), or 16 GB of RAM — the primary and only supported host platform.
Not supported
Raspberry Pi 4 and Compute Module 4.
Non-Raspberry Pi SBCs (Orange Pi, Rock Pi, Jetson, etc.), even those with a PCIe FFC interface.
Co-existence with other Pi 5 accessories: the Edge AI Expansion Board forwards the Pi 5 GPIO header upward without consuming any data pins, so a standard top-mounted HAT can still be used. The official Raspberry Pi Active Cooler is 100 % compatible. Camera Module 2 (IMX219), Camera Module 3 (IMX708), HQ Camera (IMX477), and Global Shutter Camera (IMX296) are tested.
Recommended bring-up kit
Everything needed to get a Raspberry Pi 5 and an Edge AI Expansion Board from the box to a running inference. The left column is what the Edge AI Expansion Board ships with; the right column is what to buy alongside it. The last item on the left, the DEEPX DX-M1 M.2 AI module, ships only with Variant 2; Variant 1 customers source it separately.
In the box
9–10 items
01
Edge AI Expansion Board
The baseboard itself, with the on-board USB 3.0 hub, three M.2 slots (AI, NVMe, cellular), USB-C PD input, and pogo pins for back-powering the Pi 5. 88.46 × 89.19 mm overall.
02
USB 3.0 Bridge
One piece. The internal USB Bridge PCBA that routes the M.2 NVMe and M.2 cellular slots through the on-board USB 3.0 (5 Gbps) hub.
03
PCIe FFC cable
40 × 8.5 mm. Connects the Raspberry Pi 5's external PCIe port to the Edge AI Expansion Board. Labeled RPi5 on one end and Edge AI on the other for foolproof orientation. Pin 1 arrows match the PCB markings.
04
M2.5 F-F spacers, 15 mm
Six pieces. Bottom standoffs. Lift the assembled stack off the work surface so the Edge AI Expansion Board can sit underneath the Pi 5.
05
M2.5 M-F spacers, 5 mm
Four pieces. Set the clearance between the Edge AI Expansion Board and the Raspberry Pi 5 PCB and ensure proper pogo-pin contact on the GPIO header underside.
06
M2.5 screws
Six pieces. Fasten the M2.5 spacer stack between the Raspberry Pi 5 and the Edge AI Expansion Board. Finger-tight only; over-torquing can damage the standoffs.
07
M2 flat-head screws
Three pieces. Secure the M.2 modules (AI, NVMe, cellular) into their slots. Finger-tight only.
08
M2 module plastic spacers
Three pieces. Level each M.2 module (AI, NVMe, cellular) in its slot so the module sits flush against the standoff before tightening.
09
USB-C blanking plug
Fits the Raspberry Pi 5's own USB-C port, which stays free because the Edge AI Expansion Board's USB-C PD input back-powers the Pi 5 through the pogo pins.
10
DEEPX DX-M1 M.2 AI module
Variant 2 only
One piece. The DEEPX neural processing unit on an M.2 M-Key module, up to 25 TOPS at INT8 precision, with 4 GB LPDDR5 @ 5600 MT/s on-package memory. Ships pre-bundled with Variant 2; Variant 1 buyers source the module separately and install it into the AI M.2 slot.
You'll also need
Buy separately
01
Raspberry Pi 5
4 GB, 8 GB, or 16 GB. The 8 GB variant is recommended for most workloads; step up to 16 GB for multi-stream or heavy pre-processing pipelines.
02
USB-C PD power supply
The official Raspberry Pi 27 W USB-C PD supply is the minimum. 45 W is recommended when AI, NVMe, and cellular are all populated. Standard 15 W chargers cause under-voltage warnings and SSD disconnects.
03
microSD card
Class 10 / A2, 32 GB or larger. Holds Raspberry Pi OS, dxrt-runtime, and any compiled DXNN model files.
04
Raspberry Pi Active Cooler
Recommended
The Pi 5 sits on top of the Edge AI Expansion Board; without active cooling the Pi 5 CPU can throttle under sustained load. The Edge AI Expansion Board does not have a fan connector of its own; the Active Cooler runs from the Pi 5's own JST-SH header.
05
NVMe SSD (M.2 M-Key)
Optional
2230 / 2242 / 2260 / 2280 form factors. The 256 GB and 512 GB Raspberry Pi NVMe SSDs are the validated reference; other M-Key NVMe SSDs in the four supported sizes work but are not part of the validated list.
06
LTE/5G M.2 modem & nano SIM
Optional
An M.2 Key-B cellular modem and a nano SIM (eUICC supported). Only needed for deployments that require WAN connectivity from the device itself.
07
Camera
Optional
A Raspberry Pi Camera Module, a USB camera, or an IP camera (RTSP). Only needed for live-video pipelines; recorded video files work without one.
Partnership lockups
Intelligented by DEEPX. Built on Raspberry Pi.
DEEPX silicon. Raspberry Pi host. Sixfab integration, software stack, and support.