ByteShape's device-optimized release showing superior TPS-quality tradeoffs across edge and datacenter hardware.
Qwen3-30B-A3B-Instruct-2507 device-optimized quant variants without output quality falling off a cliff.
A 30B runs on a Raspberry Pi 5 (16GB) achieving 8.03 TPS at 2.70 BPW, while retaining 94.18% of BF16 quality. ShapeLearn tends to find better TPS/quality tradeoffs versus alternatives.
What’s new/interesting in this one
CPU behavior is mostly sane
On CPUs, once you’re past “it fits,” smaller tends to be faster in a fairly monotonic way. The tradeoff curve behaves like you’d expect.
GPU behavior is quirky
On GPUs, performance depends as much on kernel choice as on memory footprint. So you often get sweet spots (especially around ~4b) where the kernels are “golden path,” and pushing lower-bit can get weird.
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