Confusion Surrounds Qwen 3.8 Release and Availability
The anticipated release of Alibaba’s Qwen 3.8 model series has been met with technical difficulties and conflicting information regarding its availability. While community discussions on platforms such as Reddit suggest a highly anticipated rollout, users have reported significant issues accessing official download links and model repositories.
Technical Access Issues and 404 Errors
Reports from the developer community indicate that the expected release of the Qwen 3.8 27B model has faced delays. According to discussions on Reddit, a previously anticipated release date—estimated by some users to be approximately 36 hours away—was not met. Users attempting to access the model via ModelScope, a platform owned by Alibaba, reported encountering 404 errors, suggesting the link is currently inactive.
Further complicating the release, users noted that the official Qwen repository on Hugging Face has not yet been updated with the new models. This has led to speculation regarding whether the absence of the files is due to a temporary server outage or a postponement of the official launch date.
Unverified Performance Benchmarks
Despite the lack of official availability, some users have shared unverified performance data involving quantized versions of the model. One user reported testing a 1-bit version of a 2.4T parameter model using Unsloth software on a Mac Ultra with 512GB of RAM. The user claimed to achieve approximately 50 tokens per second (t/s), though these figures have not been independently verified.
The reported testing included complex SVG (Scalable Vector Graphics) generation tasks. The user provided examples of the model generating images of a panda, a capybara, and a pelican. While the user noted the model successfully utilized Python to generate textures and noise for these renders, they also acknowledged limitations, such as anatomical errors in the generated figures and the high computational cost required to run the model.
Hardware Requirements and Local Deployment
As the community awaits a stable release, discussions have shifted toward the hardware requirements necessary for local deployment. Users on the r/LocalLLaMA subreddit have raised questions regarding the feasibility of running the 27B model on consumer-grade hardware.
Preliminary reports from users attempting to run quantized versions (such as Q4_K_M) on mid-range hardware, including an NVIDIA RTX 3060 with 12GB of VRAM, indicate extremely low performance, with one user reporting speeds as low as 0.5 tokens per second. This suggests that while the model may be accessible to enthusiasts with high-memory systems, standard consumer hardware may face significant bottlenecks in processing speed and token headroom.