Run Qwen 3.8 Flash Next (125B) on consumer hardware (RTX 4090) at 100T/s (github.com)

Article summary

The article discusses running a 125-billion-parameter AI model, Qwen 3.8 Flash Next, on consumer hardware, specifically an RTX 4090, using the Strata inference engine. This allows for fast and private AI processing on a personal computer. The model can be installed and set up using a simple installer, and it supports various features such as coding, chat, and image input. The article also provides information on the model's performance, including its speed and accuracy.

Main themes

• AI model performance
• Consumer hardware
• Private AI processing
• Model size and speed trade-offs
• Quantization techniques
• Long prompt processing
• Model comparisons
• Benchmarking and evaluation

What commenters say

• Some users have successfully run the Qwen 3.8 Flash Next model on their hardware and reported good performance and accuracy.
• The model's performance is compared to other models, such as Qwen 3.8 27B, with some users finding it to be superior.
• There are discussions about the trade-offs between model size, speed, and accuracy, with some users prioritizing one over the others.
• The use of quantization techniques, such as 2-bit and 4-bit quantization, can affect the model's performance and accuracy.
• Some users have reported issues with the model, such as spelling mistakes and ignoring instructions, but these may be due to configuration or hardware issues.
• The model's ability to process long prompts and contexts is also discussed, with some users reporting good results.
• There are comparisons between the Qwen 3.8 Flash Next model and other models, such as Ornith 1.5, with some users finding one to be better than the other for specific tasks.
• The importance of benchmarking and evaluating the model's performance is also highlighted, with some users calling for standardized benchmarks and evaluations.