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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteQwen documents loading Qwen3.8-Flash-Next with Transformers and device_map="auto", but its documentation does not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. Those figures should be treated as a reported runtime observation, not a guaranteed behavior of the model or automatic placement.
What Qwen documents about loading the model
The official Qwen3.8-Flash-Next model card shows a Transformers example that creates an AutoProcessor and loads the model with AutoModelForMultimodalLM.from_pretrained("Qwen/Qwen3.8-Flash-Next", device_map="auto"). The card identifies the repository as model weights and configuration files in Transformers format, and lists compatibility with Transformers, vLLM, SGLang, TokenSpeed, and other tools.
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Qwen’s Transformers documentation explains that device_map="auto" can automatically place parameters across available devices and relies on Accelerate. It also advises not to specify device_map and device together. This is general guidance; it does not promise an even split or specify which GPU index will hold model weights.
What the GPU 0 and 22 GB report establishes
The exact-title report surfaced in a secondary search summary dated 2026-10-05, but the page itself was not readable. No inspectable primary source reproduces the four-GPU result. The claim that GPU 0 remains empty and 22 GB is offloaded is therefore unverified; it is not a measurement published in Qwen’s model card or placement documentation.
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The available information does not identify what “offloads 22 GB” means in that report. It could not be responsibly attributed to CPU RAM, disk, or another allocation category without the runtime details. Nor is there enough evidence to explain why GPU 0 would be unused.
Details needed to diagnose or reproduce the placement
Automatic placement depends on the actual environment and loading configuration. A useful reproduction should include:
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- The complete
from_pretrainedcall, including anymax_memorysetting. - The model revision, dtype or quantization, and versions of Transformers, Accelerate, and PyTorch.
- GPU models, visible memory, process-visible GPU order, and
nvidia-smioutput. - The resolved
model.hf_device_mapafter loading. - GPU, CPU, and disk memory measurements taken before and after loading, along with any CPU or disk offload configuration.
Without those details, changing GPU order or memory limits would be guesswork rather than a source-supported fix. The device map and memory measurements are the key evidence for determining where model components actually landed.
Why a four-GPU vLLM example does not answer this question
A separate third-party W4A16 model card describes a vLLM deployment using four RTX 3090 cards and discusses context-length and model-feature trade-offs. That is a different checkpoint and inference stack from Qwen’s Transformers loading example. Its hardware assumptions and measurements cannot confirm or explain the reported device_map="auto" behavior.
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