Banana Pi's BPI-SM10 Brings 60 TOPS RISC-V AI to a Tiny SBC That Runs 30B Local LLMs

A RISC-V AI Board Built for Local LLMs

Banana Pi unveiled the BPI-SM10 (K3-CoM260) Developer Kit and a K3 Pico-ITX 2.5-inch industrial single board computer on April 24, 2026 — a high-end RISC-V AI platform built around the SpacemiT K3 system-on-chip with 60 TOPS of AI compute and on-device 30-billion-parameter large language model inference. For the maker community, edge AI developers, self-hosted LLM enthusiasts, and embedded engineers who have been waiting for a credible RISC-V alternative to ARM-and-x86 AI single board computers, this is one of the most ambitious SBC launches of the spring 2026 hardware cycle.

The board lineup pairs the BPI-SM10 developer kit with the K3 Pico-ITX SBC for industrial deployment, giving makers and integrators two form factors built around the same SpacemiT K3 silicon. For local LLM hobbyists who have been hitting the ceiling of what 8GB-or-16GB ARM single board computers can do, a 32GB LPDDR5 RISC-V board with native 60 TOPS AI compute is a meaningful capability step up.

The SpacemiT K3 SoC at the Heart of the Platform

The SpacemiT K3 is the centerpiece of the announcement. The chip is aligned with the RISC-V RVA23 profile — the most current RISC-V application-class profile — and packages an 8-core CPU complex paired with a separate 8-core dedicated AI compute cluster on the same die. Specifically, the K3 includes 8 X100 high-performance RISC-V general-purpose cores running up to 2.4 GHz, and 8 A100 dedicated AI compute cores that deliver the 60 TOPS of AI compute that the platform headlines.

For developers comparing to existing AI SBCs, that is a direct shot at the Raspberry Pi 5 plus Hailo AI Hat, the Orange Pi AI Pro family, and the various Rockchip-based AI development boards. The 60 TOPS number is competitive with the upper-mid tier of edge AI accelerators, and packaging both general-purpose CPU cores and AI cores on the same RISC-V SoC simplifies the software stack compared to discrete-accelerator solutions.

The memory configuration is the second standout. Both products support up to 32GB of LPDDR5-6400 memory — meaningful headroom for running 30-billion-parameter LLMs locally without the kind of swapping and quantization compromises that smaller-memory boards have to make. SpacemiT has demonstrated 30B-parameter inference on the platform at approximately 10 tokens per second, which is a usable interactive-class throughput for local LLM applications.

Power Profile and I/O for Real Deployment

Power consumption is rated between 18W and 35W depending on configuration and workload. That is a tight power envelope for a 60 TOPS AI platform — comparable to mid-range mini PCs and well below the power profile of any GPU-based local LLM solution. For battery-supplemented embedded deployments, edge gateway use cases, and home-lab self-hosted AI projects, the 18W to 35W envelope makes the BPI-SM10 viable in physical and thermal scenarios where larger GPU-based solutions are not.

The I/O is generous for a board in this class. Four USB 3.0 ports, Gigabit Ethernet, DisplayPort and MIPI-DSI for video output, and several M.2 slots for SSD expansion give the platform the connectivity needed for the kinds of applied projects RISC-V SBCs are typically used for — edge AI gateways, embedded controllers, AI-powered robotics, and self-hosted local LLM inference servers.

Why a RISC-V AI SBC Matters in 2026

The broader story behind the BPI-SM10 is the maturation of RISC-V as a credible platform for AI compute. RISC-V has been gaining ground steadily as an open-instruction-set alternative to ARM and x86, and the SpacemiT K3's RVA23-profile compliance is a meaningful signal that the RISC-V software ecosystem is ready for real applied workloads.

For makers and embedded developers, the practical implication is that RISC-V boards like the BPI-SM10 can now ship with a Linux distribution, a working AI runtime, and access to the major open-source LLM models — including DeepSeek, Qwen, Mistral, and Llama variants — without the kind of manual software-porting work that earlier RISC-V development cycles required.

The Local LLM Use Case in Focus

The 30-billion-parameter local LLM use case is the standout differentiator. For developers building applications that need on-device language understanding without relying on cloud APIs — privacy-sensitive applications, offline operation, latency-sensitive use cases, and cost-controlled internal automation — the ability to run a 30B model interactively on a single SBC is genuinely useful.

The applied use cases this enables are concrete. A local AI document assistant for regulated industries that cannot send documents to cloud APIs. An offline coding assistant for environments without reliable internet. A smart-home AI controller that handles natural language locally without round-tripping through cloud services. An edge AI gateway that performs LLM-based filtering and analysis on sensor data streams.

For self-hosted LLM enthusiasts specifically, the BPI-SM10 plus 32GB of LPDDR5 is the kind of compact, low-power, RISC-V-native AI hardware that fills a gap the maker hardware ecosystem had not previously addressed. Compared to running a 30B model on a workstation-class GPU, the BPI-SM10 trades raw inference throughput for a dramatically smaller, quieter, more power-efficient footprint.

How This Fits the Broader 2026 Edge AI Hardware Story

The BPI-SM10 launch sits inside a busy spring 2026 edge AI SBC hardware calendar. Axiomtek's PICO570 Meteor Lake SBC and AAEON's BOXER-6407-TWL fanless edge AI PC each landed earlier in the month with their own takes on the edge AI form factor. The Raspberry Pi CM0 finally reached hobbyists at $33. Beelink, GMKtec, and other mini PC vendors have continued to push consumer-and-prosumer AI PC hardware forward.

The Banana Pi BPI-SM10 distinguishes itself in that lineup specifically by bringing the RISC-V architecture and the 30B local LLM use case to the table. For embedded developers and makers evaluating their next AI hardware platform, the BPI-SM10 is a strong candidate for any project where RISC-V is preferred, where 60 TOPS of AI compute is the right capability tier, and where the on-device LLM inference use case is in scope.

For the maker hardware community at large, the broader signal is that RISC-V single board computers have meaningfully closed the gap with ARM and x86 alternatives in the AI-capable SBC category. Six months ago, picking a RISC-V AI board for a serious applied project was an experiment. Today it is a credible decision.

Sources: Liliputing (April 24, 2026), Banana Pi News (April 24, 2026), Lunar Computer (April 24, 2026), Guru3D (April 2026), Notebookcheck (April 2026)