Reddit user reports GLM and Qwen results on a dual-M5U cluster
A Reddit user reported running GLM 5.3 q6 and Qwen Flash Next q6 on two 256GB M5U units with 80 cores each. The post claims 4.9k prefill and 50 tokens per second for GLM, while Qwen reached 8k prefill and 200 tokens per second through Tensorfold; the author explicitly questioned the GLM prefill figure because it came from an LLM's explanation. The author said DeepSeek configured oMLX from guides and asked the community for optimization advice.
Although unverified, the post gives prospective M5U buyers a practical, model-specific local-inference datapoint while clearly documenting the limits of its setup and measurement method.
Caveats: IDK how to set anything up and I asked Deepseek to handle it - not enough training data so without specific guides the set up doesn't work well. I'm not a programmer. My previous model was Qwen 3.8 27B q4 using ninfer. Was getting around 1.4k prefill and 100-120 tokens decode.
Just want to provide data for anyone interested in purchasing M5U 256gb (80 cores) x 2. And if you have suggestions on how to optimize my set up I'd really appreciate it.
- GLM 5.3 q6 cluster: prefill 4.9k (idk if this is right but this is what the LLM says, two thunderbolt 5 cables), decode: 50 tok/s. oMLX set up with Deepseek API.
- Qwen Flash Next q6 cluster: prefill 8k and decode 200 using Tensorfold. I copied their guide, sent it to Deepseek, and asked it to set it up.
GLM was too slow for me so I am using Qwen Flash Next.
I am using Hermes as the harness (hermes has deepseek harness as research agent and Pi harness for coding agent). I am wondering whether using a different harness with less context overhead would make things faster or if since it is mostly context it doesn't really make a difference.
submitted by /u/Viktri1 [link] [comments]
#M5U#GLM 5.3 q6#Qwen Flash Next q6#Qwen 3.8 27B q4#oMLX#Tensorfold#Hermes#DeepSeek API#n Infer