RTX 3090 24GB รัน Qwen4 ได้จริงไหม: ตัวเลขที่โพสต์ไม่บอก

An X post from Nov 10, 2026 claimed that an RTX 3090 with 24GB of VRAM could run Qwen 4, a 27B model, with ease. The post was shared because it seemed to align with the fantasies of users who already own powerful graphics cards. Upon checking the Hugging Face repository of Qwen, it was found that while some aspects were true, the model name and the term "Flash" led to misunderstandings.

Two relevant model versions were present: Qwen3.8-27B, released in August 2026, and Qwen3.8-Flash-Next, released in August 2026. No Qwen4-27B model existed in the official organization. The term "Qwen 4" used in the post was derived from the Flash-Next framework, named "qwen4_exp". The 27B version with a Flash-Next structure was available, with a Qwen3_5 base.

This means that the person who posted might have based their expectations on a teaser, which was then referenced in the post. It was clear that the 27B model running on a 24GB card was feasible, but it required reducing the model's detail, using bf16 format, and potentially a reduced precision of 4 bits. The resulting file size would be about 16.7GB due to 4,294,966,368 parameters, leaving only 7GB free for the key-value cache, which would increase with longer prompts.

The post's use of the term "running clean" depended on the context size and the quantization technique used. The Flash-Next model had 180 million parameters and a total file size of 360GB, meaning that even with 2-bit quantization, it would still require around 45GB, nearly double the 24GB of an RTX 3090. The Flash version available to the public was a service provided on the cloud, not a model that could be downloaded and run on a graphics card.

The interest in these numbers stems from the fact that the RTX 3090's memory bandwidth, at 936GB per second, is high enough to influence the speed of generating tokens, potentially more than the number of parameters in some cases. Transformers, the type of model used here, process context through token-based attention layers with each response.

The computational complexity is high, requiring frequent access to memory, which is crucial for token generation. The RTX 3090's two memory channels can handle up to 936GB per second, making it a popular choice for running models locally. However, the 24GB VRAM limit is a significant bottleneck, and newer models are growing faster than the cards can keep up.

To test running a 27B model locally on an RTX 3090, one could use compressed formats like GGUF or AWQ, start with smaller context sizes, measure the actual token-per-second rate, and evaluate the impact of quantization on performance. It's important to note that the posted token rates often reflect the tester's conditions and not necessarily those of others.

No independent testing of Qwen3.8-27B on an RTX 3090 with actual speed and quality results had been reported. The claim in the post could be true, but it was based on a teaser rather than an actual model release, leading to exaggerated expectations.