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For Qwen3.8-27B, the answer depends on the checkpoint format: the vLLM project lists VRAM floors of 24 GB for INT4, 32 GB for NVIDIA NVFP4, 38 GB for its official block-scaled FP8 checkpoint, and 67 GB for BF16. These are recipe-specific planning figures, not guarantees for every context length or workload. Weights are only part of the memory budget.
How much VRAM does each Qwen3.8-27B format need?
The vLLM project’s live recipe, accessed October 7, 2026, gives the following minimum VRAM figures for particular checkpoints and deployment configurations. They should not be read as universal minimums for every GPU, runtime, or request pattern.
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| Checkpoint format | Checkpoint size reported by vLLM | Recipe VRAM floor | Important qualification |
|---|---|---|---|
| BF16 | 55,563,006,776 bytes: 55.6 GB on disk, or 51.7 GiB of weights | 67 GB | The recipe describes the BF16 case as one GPU. [vLLM recipe] |
| Official block-scaled FP8 | 30,866,866,928 bytes: 30.9 GB on disk, or 28.7 GiB of weights | 38 GB | Applies to the recipe’s named FP8 checkpoint, not every FP8 conversion. [vLLM recipe] |
| NVIDIA NVFP4 | Not stated in the recipe figures | 32 GB | The recipe lists an NVIDIA NVFP4 checkpoint on RTX 5090 hardware. Its hardware-specific launch uses a 32,768-token maximum model length, FP8 KV cache, and eager execution. [vLLM recipe] |
| Red Hat AI INT4 W4A16 | Not stated in the recipe figures | 24 GB | The recipe lists Hopper hardware among supported platforms. [vLLM recipe] |
The BF16 and FP8 checkpoint files are smaller than their corresponding VRAM floors. That difference is expected: file size measures stored weights, while inference also needs memory for runtime overhead and other active data. The project describes its floors as estimates based on checkpoint bytes and a multiplier, with hardware-specific settings and exceptions.
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Will Qwen3.8-27B run on your GPU?
Start with the recipe row that matches the exact checkpoint you plan to use, then check whether your system has that much available GPU memory. A GPU labeled with a given capacity may have less free when the display, other applications, or another model is using it. Even if the recipe floor fits, it does not establish that your intended context length, concurrency, or multimodal workload will fit too.
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- 24 GB: the listed INT4 W4A16 route has a 24 GB recipe floor; compatibility still depends on the specific checkpoint, supported hardware, and runtime.
- 32 GB: the listed NVFP4 route has a 32 GB floor. The recipe’s RTX 5090 example also specifies a 32,768-token maximum length, FP8 KV cache, and eager execution.
- 38 GB: the listed official block-scaled FP8 checkpoint has a 38 GB floor.
- 67 GB: the listed BF16 configuration has a 67 GB floor.
An RTX 5090 is one hardware example associated with the recipe’s NVIDIA NVFP4 checkpoint; that does not make it the only way to run Qwen3.8-27B or establish a purchase recommendation.
Why context length and workload change the answer
Inference memory is not just the model’s weights. The key practical variables include the maximum context, KV-cache precision, simultaneous requests, and runtime-specific overhead. Longer contexts and more concurrent work can raise memory demand, so a configuration that clears a recipe floor may still fail under your actual serving settings.
Qwen3.8-27B is a dense vision-language model that understands images and videos, according to the Qwen model card. Using its vision capabilities is part of the workload to account for; the cited VRAM floors do not establish one universal memory requirement for every image or video workload.
Qwen’s repository includes vLLM and SGLang examples configured for a 262,144-token maximum model length and tensor parallelism across four devices. That is an example multi-GPU serving setup, not evidence that one consumer GPU can provide the same context. [Qwen repository]
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How to choose a format and verify the setup
- Choose the exact checkpoint. Match the format you intend to run—BF16, official block-scaled FP8, NVFP4, or INT4—to the corresponding recipe entry. Do not infer every quantized checkpoint’s size by multiplying parameter count by an assumed bit width: vLLM warns that quantized checkpoints do not all use a uniform four bits per weight.
- Check runtime and GPU compatibility. Confirm that the checkpoint is supported by your serving software and hardware. Qwen’s model card lists Transformers, vLLM, SGLang, TokenSpeed, and other tools as compatible, but compatibility for a model family does not guarantee that every checkpoint and setting works on every GPU.
- Set the workload you actually need. Verify maximum context, KV-cache type and precision, batch size, concurrency, and whether you will process images or video. Use the NVFP4 recipe’s stated settings as an example of why its floor is tied to a particular configuration.
- Leave memory headroom. Do not plan to consume all reported VRAM with weights. Account for other GPU processes and the rest of the inference workload, then test the exact configuration you intend to serve.
The Qwen model card labels the repository license Apache-2.0; review the model card and any checkpoint-specific terms for your intended use. [Qwen model card]
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