Do these 3 things before closing this tab:
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driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteYes. Core vLLM documents GGUF support on NVIDIA GPUs using Volta, Turing, Ampere, Ada, and Hopper architectures. Its current compatibility table does not list GGUF support for AMD or Intel GPUs, or for x86 and Arm CPUs. GGUF support is experimental, so check the documentation for your vLLM release and model before building a setup around it.
Where core vLLM supports GGUF
The current vLLM GGUF documentation and quantization compatibility table describe GGUF as supported on these NVIDIA GPU architectures:
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- Volta
- Turing
- Ampere
- Ada
- Hopper
The same table marks GGUF unsupported on AMD GPU, Intel GPU, x86 CPU, and Arm CPU. This is specific to GGUF in core vLLM; it does not mean vLLM cannot run on a CPU at all. The project has separate CPU installation documentation, but general CPU inference support does not imply GGUF-on-CPU support.
The compatibility chart can change between releases. These are the architectures listed in the current stable documentation, not a guarantee for every GPU model, GGUF file, or vLLM version.
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How to serve a GGUF model
The vLLM v0.18.1 GGUF page documents two ways to point to weights: a Hugging Face repository and quantization reference, or a local GGUF file. Both examples specify the tokenizer from the matching base model.
Use a Hugging Face GGUF reference
vllm serve unsloth/Qwen3-0.6B-GGUF:Q4_K_M --tokenizer Qwen/Qwen3-0.6BUse a local GGUF file
vllm serve ./Qwen3-0.6B-Q4_K_M.gguf --tokenizer Qwen/Qwen3-0.6BThese are examples from the official v0.18.1 documentation, not a guarantee that every model uses the same repository naming or tokenizer. For a two-GPU setup, the page shows adding --tensor-parallel-size 2. That example does not establish a minimum number of GPUs or a memory requirement for other models.
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Prepare the model files and tokenizer
Use one GGUF file
Core vLLM’s documented loader does not support multi-file GGUF models. If a repository contains split files, the GGUF documentation says to merge them with gguf-split before loading.
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When available, pass the tokenizer for the model the GGUF was converted from, as in the commands above. vLLM warns that converting tokenizer information from GGUF can be slow and unstable, particularly when the vocabulary is large.
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Supply a compatible config if needed
If Hugging Face cannot convert GGUF metadata into a compatible model configuration, the official page documents using --hf-config-path to provide a Hugging Face-compatible config. The needed config depends on the model; the documentation does not prescribe one universal file.
What “GPU only” does—and does not—mean
For the core vLLM GGUF compatibility table, the supported entries are NVIDIA GPU architectures and CPU entries are marked unsupported. That is the basis for saying “GPU only”; it should not be read as a claim about every project or plugin using the vLLM name.
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For example, vllm-metal is a separately maintained plugin that documents GGUF support through MLX. Its listed scope differs from core vLLM:
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|---|---|---|
| Core vLLM | GGUF on NVIDIA Volta, Turing, Ampere, Ada, and Hopper GPUs, according to the current compatibility table. | Experimental; multi-file GGUF is unsupported by the loader. |
| vllm-metal plugin | MLX-based support documented for Qwen2, Qwen3, Llama, and Mistral dense decoder checkpoints, with Q8_0, Q4_0, and Q4_1 quantizations. | Its page lists K-quants, MoE, SSM or hybrid models, vision models, fused-QKV GGUFs, and sharded GGUFs as unsupported. |
The plugin’s support does not broaden core vLLM’s hardware or model compatibility. Consult the relevant project’s documentation for the exact release and model you plan to use.
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Experimental status and practical limits
The core vLLM GGUF page calls support “highly experimental and under-optimized” and warns that it may be incompatible with other features. Treat the documented commands as a starting point, not a blanket promise that any GGUF model will work.
The documentation presents GGUF chiefly as a way to reduce memory footprint. It does not establish a universal VRAM minimum, nor does it provide a performance comparison showing how GGUF fares against other vLLM model formats in speed, output quality, or feature coverage. Those results should not be inferred from the compatibility table.
Quick Recap
Check before choosing hardware or a model
- Confirm that the GPU architecture appears in the GGUF compatibility table for the vLLM release you intend to use.
- Check that the model’s GGUF quantization and architecture are supported by your chosen route; a supported GPU alone does not guarantee compatibility.
- Verify that the model can be loaded as one GGUF file, or plan to merge split files with
gguf-split. - Locate the matching base-model tokenizer and a compatible Hugging Face config if the model needs one.
- Check model and memory requirements separately. The compatibility documentation does not specify a universal VRAM minimum or recommend a particular graphics card.
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