January 6, 2026
Pi fights, bytes fly
A 30B Qwen Model Walks into a Raspberry Pi and Runs in Real Time
Fans cheer, skeptics jeer: Pi ‘real-time’ claim meets segfaults and accuracy beef
TLDR: ByteShape claims a huge 30B AI runs “real-time” on a Raspberry Pi 5, hitting about 8 tokens per second with near-full quality. Comments split between hype and hiccups: some dream of local Alexa, while others hit memory errors and question the accuracy math, making this a must-watch DIY moment.
ByteShape just dropped a spicy claim: a massive 30B Qwen AI model runs on a Raspberry Pi 5 in real time. They say it spits out around 8 tokens per second (TPS)—about reading speed—while keeping roughly 94% of “full-quality” results. The pitch: smart number-shrinking (bitlength learning) that treats memory as a budget, then optimizes for speed and quality. Cue the comments: some are celebrating a future of cheap, private AI on the kitchen counter; others are checking the receipts.
One commenter helpfully defines “real-time” as that ~8 TPS mark, but the vibe flips fast. Havoc side-eyes the math, asking if this accuracy is measured differently—how does dropping precision lose only ~5%? Then the replication drama hits: geerlingguy tries it on a Pi 5 and gets a big, ugly memory error. Instantly, the thread turns into: miracle demo or misconfigured fantasy? Meanwhile, jmward01 jokes there’s a “tens of dollars” market for a local Alexa, and lostmsu chimes in with, “Just use a smaller 20B model.” Brand skirmishes flare: ByteShape vs Unsloth vs MagicQuant—who really wins on speed vs quality? The mood is pure tech soap opera: Schrödinger’s Pi—it’s both blazing-fast and faceplanting, depending on your setup. If the promise holds, an $80 board doing big-league AI could be the DIY dream. Try it yourself: link.
Key Points
- •ByteShape optimized Qwen3-30B-A3B-Instruct-2507 using Shapelearn to select weight datatypes that fit memory and maximize TPS and quality.
- •On Raspberry Pi 5 (16GB), the Q3_K_S-2.70bpw [KQ-2] build achieves 8.03 TPS at 2.70 BPW with 94.18% of BF16 quality, enabling real-time interaction.
- •In llama.cpp, fewer bits do not automatically increase speed; kernel and overhead differences can make lower-bit formats slower on some GPUs.
- •On CPUs, once a model fits in RAM, reducing bitlength generally increases TPS, enabling predictable speed–quality tradeoffs.
- •ByteShape reports lower error (~1.1–1.3% vs. ~2.1–2.2% for Unsloth) among Pi-fit models, including up to 1.87× lower error than Unsloth’s UD-Q3_K_XL [8] at ~5–6 TPS.