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For a comfortable quantized setup, target at least 24 GB of graphics memory—VRAM on a discrete GPU or variable graphics memory (VGM) on a supported system. The exact fit depends on the checkpoint, context length, runtime and serving configuration. For vLLM’s listed configurations, the recommended minimums range from 24 GB for INT4 to 67 GB for BF16. System RAM is separate, and the available sources do not establish one universal system-RAM minimum.
How much graphics memory does Qwen3.8-27B need?
There is no single memory number for every Qwen3.8-27B setup. The model’s weight footprint changes with precision and quantization, and weights are only part of the working-memory budget: the KV cache grows with context, while the runtime and operating system also use memory.
The following are configuration-specific figures from the vLLM Project’s maintained deployment recipe. The listed weight sizes describe the checkpoints; the VRAM figures are recipe recommendations, not guarantees that every context length or workload will fit.
| Configuration | Listed weight size | vLLM recipe minimum VRAM |
|---|---|---|
| INT4 W4A16 | 19.5 GB | 24 GB |
| NVFP4 build (21.9 GB checkpoint) | 21.9 GB | 32 GB |
| NVFP4 build (26.4 GB checkpoint) | 26.4 GB | 32 GB |
| Official block-scaled FP8 | 30,866,866,928 bytes (30.9 GB; 28.7 GiB) | 38 GB |
| BF16 | 55,563,006,776 bytes (55.6 GB; 51.7 GiB) | 67 GB |
These figures come from the vLLM Project Qwen3.8-27B deployment recipe. Quantized options are not all interchangeable or uniformly four-bit; hardware support varies by build. Check the recipe’s model variant and hardware requirements before choosing a card.
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Which GPUs are documented for this model?
AMD: Radeon AI PRO R9700 or Ryzen AI Max+
AMD says Qwen3.8-27B needs roughly 24 GB of VGM or VRAM to run comfortably and identifies its 32 GB Radeon AI PRO R9700 as a supported single-card option. AMD also lists Ryzen AI Max+ processor-based systems, which use variable graphics memory. This makes the R9700 a specifically documented discrete-GPU choice, but the guidance does not mean every model configuration or context will fit in 24 GB.
AMD reports preliminary Windows/Vulkan llama.cpp results of up to 51.8 tokens per second on the Radeon AI PRO R9700 and up to 24.5 tokens per second on Ryzen AI Max+ 395. These are manufacturer-reported figures: AMD says each average covered at least three runs, used MTP=2 on the Radeon and MTP=4 on the Ryzen system, and may vary. They are not a controlled cross-vendor comparison. AMD’s test systems had 64 GB of system memory for the Radeon test and 128 GB for the Ryzen test; those are test configurations, not stated minimums. Details are in AMD’s model deployment and performance post.
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NVIDIA: RTX 5090 for a documented NVFP4 path
The vLLM recipe documents an NVFP4 deployment on one RTX 5090, with a 32K maximum context and FP8 KV cache. That single-card example requires --enforce-eager: the recipe says CUDA graph capture runs out of memory without it. The same recipe describes a two-RTX-5090 path for a larger context. Treat these as recipe-specific setups, not a blanket claim that any RTX 5090 configuration will support any context or serving workload.
How to choose a setup
- Choose the model precision or quantized build. Use the vLLM recipe’s VRAM recommendation for that exact variant; do not infer memory needs from the “27B” parameter count alone.
- Match memory to your intended context. Longer contexts require more KV-cache memory. Leave room beyond the checkpoint’s weight size for cache, runtime and operating-system use.
- Verify runtime and hardware support. Confirm that the desired build and inference software support your GPU or VGM platform. For the documented single-card RTX 5090 NVFP4 example, use the stated eager-mode setting.
- Decide whether you need one card or a larger system. AMD names a 32 GB single-card option; vLLM documents an RTX 5090 single-card example and a two-card route for larger context. Compare form factor, memory capacity and the specific configuration you plan to run.
What about system RAM?
System RAM and GPU memory are distinct resources. A workload that offloads model data or uses unified memory can make system RAM relevant, but the sources here do not establish a universal minimum for running Qwen3.8-27B across runtimes and configurations. Do not treat AMD’s 64 GB and 128 GB benchmark-machine capacities as requirements; they describe the machines used for those tests.
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What the neighboring-model estimates can—and cannot—tell you
An Alibaba Cloud Community article dated August 5, 2026, measured Qwen3.6-27B weights at 55.6 GB for BF16, 28.6 GB for Q8_0 and 16.8 GB for Q4_K_M. Those are Qwen3.6 measurements, not Qwen3.8-27B checkpoint sizes or direct memory requirements. The article uses them as adjacent-model estimates and explains that KV cache, inference runtime and operating-system use add to weight memory; cache use rises with context. Use the Qwen3.8-specific vLLM recipe figures above for configuration planning, rather than substituting these neighboring-model values. The article is available at Alibaba Cloud Community.
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