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Qwen documents loading Qwen3.8-Flash-Next with Transformers and device_map="auto", but its documentation does not verify that GPU 0 stays empty or that 22 GB is offloaded. Treat those details as an unconfirmed runtime report—not as expected behavior of the model or automatic placement. To diagnose the result, you need the actual device map and the loading environment.
What Qwen’s documentation establishes
The official Qwen3.8-Flash-Next model card includes a Transformers example that loads the model with AutoModelForMultimodalLM.from_pretrained("Qwen/Qwen3.8-Flash-Next", device_map="auto"). It also identifies the repository as model weights and configuration files in Transformers format, and lists compatibility with other inference tools.
Qwen’s Transformers documentation describes device_map="auto" as automatic placement of parameters across available devices and says the feature relies on Accelerate. It advises against setting both device_map and device in the same call. This is general placement guidance; it does not promise an even split or specify that weights will be assigned to a particular GPU index.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallWhat the GPU 0 and 22 GB report does not establish
The reported observation—that GPU 0 is empty and 22 GB is offloaded—has not been verified by the official model card or Qwen’s placement documentation. The available account does not identify the model revision, software versions, GPU models, visible memory, configured memory limits, or the resolved model.hf_device_map. It also does not define whether “offloaded” means CPU RAM, disk, or another allocation category.
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Without those details, there is no evidence-based way to explain why GPU 0 received no model layers or where the reported 22 GB went. In particular, the observation does not show that device_map="auto" inherently skips GPU 0 or that Qwen3.8-Flash-Next necessarily offloads that amount.
Collect the details needed to reproduce placement
Share the following information alongside the result. Together, it can distinguish a device-visibility or memory-limit issue from a placement or offload report, without presuming which one occurred.
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- Chipset: GeForce RTX 3050
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- The complete load call: include the model class, checkpoint identifier, revision, dtype or quantization settings, and any
device_map,max_memory, CPU-offload, or disk-offload options. - Library versions: record the versions of Transformers, Accelerate, and PyTorch used for the run.
- Visible GPU inventory and ordering: capture
nvidia-smioutput and explain which GPUs the process can see. GPU index labels can only be interpreted in the context of that process-visible order. - The resolved placement: print
model.hf_device_mapafter loading so the assigned modules and devices are visible. - Memory measurements: record GPU, CPU, and disk memory before and after loading, and state how the 22 GB figure was measured and what category it represents.
These are the missing details required to make the report reproducible; they are not evidence of a specific cause.
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A separate third-party W4A16 model card describes a vLLM deployment using four RTX 3090 cards, with trade-offs involving context length and model features. That is a different checkpoint and inference setup from Qwen’s Transformers loading example, so its hardware or memory figures cannot explain this report. The available evidence does not support a hardware purchase recommendation.
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