ByteShape demonstrates edge inference readiness with a 30B Qwen3 running on Raspberry Pi 5 16GB RAM, achieving 8.03 TPS at 2.70 BPW and 94.18% BF16 quality.
AI Team
Continue your reading
ByteShape Qwen3 30B Real-Time Edge Inference on Raspberry Pi 5
ByteShape makes a provocative claim: a 30B Qwen3 model can run in real time on a Raspberry Pi 5 with 16GB of RAM. Their published config shows 8.03 TPS at 2.70 BPW with 94.18% BF16 quality, a result they call genuinely real-time on constrained hardware. The takeaway is simple: edge inference isn’t a fairy tale anymore, not on a Pi with enough memory to hold a compact yet capable 30B model. ByteShape frames this as a practical demonstration of what actually matters to users in the wild: speed and output quality on the target device, not theoretical throughput on rack-mounted GPUs.
Behind the numbers is a technique they call Shapelearn, a bitlength learning method to pick weight datatypes that maximize TPS while staying within memory budgets. The core principle is to treat memory as a budget and balance speed with quality instead of chasing the smallest weights. This matters because quantization isn't a magic lever: in llama.cpp, fewer bits don't automatically mean faster performance. Different quant formats trigger different kernels and overheads, and on some GPUs cutting bits can even slow you down despite using less memory. That nuance is why ByteShape focuses on a measured balance rather than blanket compression.
In their build you see the practical result: a Qwen3-30B-A3B-Instruct-2507 family model running on a Pi 5 with a memory footprint. The bottom line: yes, this Qwen3 can run on a Raspberry Pi 5. On the Pi 5, the config labeled Q3_K_S-2.70bpw [KQ-2] hits 8.03 TPS while preserving 94.18% BF16 quality. It feels real-time because the system is tuned to real hardware constraints, not a theoretical peak. ByteShape's broader claim is that their models show a pattern you can push edge devices toward usable latency without sacrificing the user experience more than necessary.
That caveat is real. The same pattern ByteShape reports isn't a universal guarantee across devices or models. Kernel overheads that vary by GPU mean you can’t assume that reducing bits will always speed things up. llama.cpp and similar projects show that edge speedups depend on the exact kernel implementations and the hardware's memory bandwidth, cache behavior, and parallelism. ByteShape's claim rests on a carefully tuned setup with Qwen3-30B-A3B-Instruct-2507, the Shapelearn strategy, and the Pi 5's memory profile; your mileage will vary if you swap model families or hardware.
If you want to dig deeper, check out ByteShape's own writeup for the Qwen3-30B-A3B-Instruct-2507 configuration, and explore related hardware and tooling pages to compare with other edge inference efforts. You can read the ByteShape post directly here, and see their broader work on on-device models at the company's site. For hardware context, the Raspberry Pi 5 product page is a good reference, and the llama.cpp project provides a grounded comparison point for quantization and edge performance. A 30B Qwen Model Walks Into a Raspberry Pi ByteShape Raspberry Pi 5 llama.cpp GitHub