PROPOSAL: Latte Panda Mu #86
Description
I took a challenge to design a board for the latte panda mu compute module with N305 X86 processor and 16GB of LPDDR5 RAM.
Reason is Intel makes drivers for their iGPU, and this module is dog compatible in size and power.
Reason is I tried to find accelerators for the Pi, but it's too early for that. none of the half a dozen startup I queried answered, and Hailo is just 2GB.
Cost is likely going to be 300€ for 8GB modules when they eventually come out. Meanwhile intel mobile processors have 16GB ram and drivers for their iGPU acceleration and cost less. None of the upcoming accelerators even bother using GDDR6 or GDDR7 memory, which is baffling to me. If they stick to LPDDR5 they don't have much of an edge over APUs, if any. They can do smart L3 cache thingies but that is no sobstitute for having just faster ram. I guess it makes the PCB more expensive and there aren't good memory controller IP to choose from so they stick to LPDDR5.
Reason is I want to make a pupper with local AI intelligence to do useful applications with it
My plan is to make a robot dog board, but with the power to run locally the AI
Is anyone interested in contributing on the software side? In theory, as long as you target X86 with UHD graphics on linux, it should work on latte panda mu.
Features I'm planning:
- 18650 batteries
- Pupper motors
- Multiple ESP32s for motors and AT commands for WiFi radio
- CSI for raspicam
- Ubuntu 22
- LLM brain with audio, text and image models local
- OpenVINO
- MCP
- ROS2
- Audio array
- IMU
- USB dongle for LTE connectivity
| Component | Content | Specs. |
|---|---|---|
| Processor | CPU | Intel®Processor N100 / Intel® Core™ i3-N305 |
| Cores / Threads | 4-Core, 4-Thread / 8-Core, 8-Thread | |
| Max Turbo Frequency | 3.4GHz / 3.8GHz | |
| Cache | 6MB | |
| TDP | 6 35W / 9 35W configurable | |
| Graphics | Intel® UHD Graphics | |
| Max Dynamic Frequency | 750MHz / 1.25GHz | |
| Execution Units | 24 / 32 | |
| Memory | Capacity | 8GB LPDDR5 4800MT/s / 16GB LPDDR5 4800MT/s |
| ECC Memory Support | With In-band ECC | |
| Storage | eMMC | 64GB eMMC 5.1 Speed: HS400 |
| Expansion | Expansion Capability | Up to 9x PCle 3.0 Lane Up to 4x USB 3.2 10Gbps Up to 2x SATA 6Gbps 8x USB 2.0 4x UART 4x I2C Expandable to 64x GPIO |
| Display | eDP | 1x eDP 1.4(onboard), up to 4K 60Hz |
| HDMI | 3x HDMI 2.0/DisplayPort 1.4, up to 4K 60Hz | |
| Max Outputs | 3 Ouputs; Max Resolution 4096 x 2160@60Hz | |
| Operating System | Microsoft Windows | Windows 10, Windows 11 |
| Linux | Ubuntu 22.04 & 24.04 | |
| BIOS | BIOS Info | AMI UEFI 128Mbit SPI BIOS |
| Power | Power Supply | DC 9-20V |
| RTC Battery | 3V | |
| Environment | Operating Temperature | 0~60℃ |
| Relative Humidity | 0%~80%RH | |
| Dimension | Form Factor | 60mm x 69.6mm |
| Model(SKU) | DFR1146 | LattePanda Mu (N100, 8GB RAM, 64GB eMMC) |
| DFR1146-ENT | LattePanda Mu (N100, 8GB RAM, 64GB eMMC, with Win11 Enterprise License) | |
| DFR1147 | LattePanda Mu (N100, 16GB RAM, 64GB eMMC) | |
| DFR1149 | LattePanda Mu (N305, 16GB RAM, 64GB eMMC) | |
| Compliance | Certification | FCC, CE, UKCA, RoHS, KC, Safety |
Activity
OrsoEric commentedon Sep 15, 2025
It's an idea that I have been working on for a long while
This version I made for the thesis
An upgraded version I did
Then I met MangDang and helped him with the second pupper board
Acceleration on the Pi is sad, memory bandwidth is poo poo, and I haven't even found binaries for ONNX NEON, let alone that sad excuse for an iGPU the VideoCore. It has a great x264 transcoder for the raspicam, but that's about it. There are competitor for the Pi like Rock 5B that are vastly superior in performance per dollar, and performance, but still cost 200 $ and are ARM with proprietary blob, and I see reports of mixed success accelerating things on it.
My problem is that nobody can make decent acceleration, except Nvidia and Intel. And Nvidia needs ALL the money, while Intel especially as of late with the Arc tiles has taken second place overtaking AMD in acceleration, so I'm switching SBC and using an intel atom from lattepanda. the reason I got that is because they have a challenge, and gave me a 200 $ voucher to get it. They also gave me a 500$ voucher for the board, component and assembly service.
I have been using laptop with LLM and doing lots of testing. At 40GB/s the N305 should provide quite impressive performance, and with 16GB of unified memory I should be able to run 3B and 7B models without breaking a sweat in the 10 to 40 T/s range and run an ASR model with ram to spare. This should be viable. This week I'll be powering up the latte panda install ubuntu and do preliminary tests to see if Intel does really provide acceleration on Atom.
gmacario commentedon Sep 15, 2025
Hi @OrsoEric, that sounds like an exciting proposal!
I volunteer to help and port the FREISA-GPT stack to your new robot hardware.
However I would need some additional clarifications:
I think that - once those points are clear and agreed upon - other people from the @B-AROL-O/freisa-gpt_team and/or @B-AROL-O/freisa-supporters could consider joining your project and speed up the development.
OrsoEric commentedon Sep 19, 2025
UPDATE 2025-09-17
Despite many attempts, the Lattepanda mu N305 16GB doesn't power on. The power rails are dead. I believe I got a dead board, bummer.
I ordered an 8GB board to check if I'm doing something wrong, it'll likely arrive today.
My plan is to test LLMs on the N100 8GB and that will tell us the viability of the N100 8GB and N100 16GB and N305 16GB for local AI on a mobile robot.
As suggested by @gmacario a short term goal would be to install the Latte Panda Mu on board on a wheeled robot for the Linux Day.
Once I see that this path is viable, I'll propose a conf call to discuss the project with who'd like to contribute.
The proposal in case I succeed to power on the Mu and test LLM performance, is for you to help me port what you did for FREISA-GPT onto to X86
I'll take care of making a pupper grade board for the Lattepanda Mu, idea for features is as follow
gmacario commentedon Sep 19, 2025
FYI: Our talk to LDTO25 was accepted - please see https://github.com/B-AROL-O/openai-devpost-hackathon/issues/41 for details
OrsoEric commentedon Oct 28, 2025
Proposal for the MCP challenge
have a repo in BAROLO of a robot
Track: multimodal application
sound/image to sound/move
An MVP application doesn't use the ROS2 backbone, and perhaps single LLM to simplify things.
I imagine something like this:
"Puppy, describe me what you see."
"I see ..."
"Puppy, rotate 30° clockwise and advance 50cm"
"VROOOM"
gmacario commentedon Nov 4, 2025
Reassigning to @OrsoEric as project lead