 | Hey r/LocalLLaMA We’ve released our ByteShape Qwen 3.5 9B quantizations. Read our Blog / Download Models The goal is not just to publish files, but to compare our quants against other popular quantized variants and the original model, and see which quality, speed, and size trade-offs actually hold up across hardware. For this release, we benchmarked across a wide range of devices: 5090, 4080, 3090, 5060Ti, plus Intel i7, Ultra 7, Ryzen 9, and RIP5 (yes, not RPi5 16GB, skip this model on the Pi this time…). Across GPUs, the story is surprisingly consistent. The same few ByteShape models keep showing up as the best trade-offs across devices. However, here’s the key finding for this release: Across CPUs, things are much less uniform. Each CPU had its own favorite models and clear dislikes, so we are releasing variants for all of them and highlighting the best ones in the plots. The broader point is clear: optimization really needs to be done for the exact device. A model that runs well on one CPU can run surprisingly badly on another. TL;DR in practice for GPU:
And TL;DR for CPU: really really check our blog’s interactive graphs and pick the models based on what is closer to your hardware. So the key takeaway:
The blog has the full graphs across multiple hardware types, plus more detailed comparisons and methodology. We will keep Reddit short, so if you want to pick the best model for your hardware, check the blog and interactive graphs. This is our first Qwen 3.5 drop, with more coming soon. [link] [comments] |
ByteShape Qwen 3.5 9B: A Guide to Picking the Best Quant for Your Hardware
Reddit r/LocalLLaMA / 4/1/2026
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Key Points
- ByteShape has released new quantized versions of Qwen 3.5 9B and positions the release around benchmarking quality, speed, and size trade-offs against the original and other quant variants.
- The benchmarking spans multiple GPUs (e.g., RTX 5090/4080/3090/5060 Ti) and several CPUs (e.g., Intel i7, Ultra 7, Ryzen 9, and Raspberry Pi 5 as a special note), with comparisons intended to guide hardware-specific selection.
- GPU results are reported as relatively consistent, with the same small set of ByteShape quantizations repeatedly offering strong quality/efficiency trade-offs across devices.
- A key takeaway is that CPU performance is far less uniform across processors, requiring optimization and careful model/quant selection for the exact CPU to avoid cases where a quant that works well on one CPU performs poorly on another.
- For practical picks, the guide recommends specific GPU-oriented quant levels (near-baseline at ~5.10 bpw, balanced at ~4.43 bpw, and faster at ~3.60 bpw) and emphasizes verifying CPU performance using the provided plots.
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