Unsloth has quants for both
- Unsloth Qwen3 Next guide
- unsloth/Qwen3-Next-80B-A3B-Thinking-GGUF
- unsloth/Qwen3-Next-80B-A3B-Instruct-GGUF
This was a great day to check the news. I also saw that vllm has just added support for strix-halo.
How much RAM does something like that need?
Don’t MoE models just load into memory as every other model and it’s just that they pick a subset of numbers to multiply by each step so they’re faster? That’d make me think it’d need somewhere around 80GB of memory at 8bit or 160GB at full precision, or something like 50GB at the average llama.cpp Q4_K_M…
That is correct, but you might be missing why this is useful. MoE models are great for CPU inference, which is considerably cheaper than GPU inference at scale. The qwen 30b_a3b MoE and 8b dense models were widely considered similar in quality. If you have the vram, the 8b would be faster. If you don’t, then the 30b would be faster (as long as you had the ~19-22gb of ram required)
A very inexpensive used server with lots of memory channels but no gpu can do very cost-efficent inference in this scenario and loads of people are asking for this.
Fantastic explaination, thank you
So not an AMD AM5 dual-channel system. 😅
Ooo rocm support!
