ComfyUI GGUF “Failed to Find the Quantized Version”: Fix the Loader, Dependency, and CUDA Backend Chain

What the Error Actually Means

If you run ComfyUI with the ComfyUI-GGUF custom node and see a line like this in your terminal or node console, you are not alone:

Failed to find the quantized version of the model
 or
AssertionError: Torch not compiled with CUDA enabled

The first message is the GGUF loader failing to locate a model file it can load. The second is a related class of failure that surfaces when the quantized weights cannot be handed to an available CUDA backend. Both share a common root cause chain: the custom node expects a specific file type, a specific dependency version, and a specific loader contract, and when any one of those is off, the node quits before any generation begins.

ComfyUI-GGUF is the community-standard bridge that lets low-VRAM cards run large diffusion and large-language models that would otherwise not fit. It works by loading quantized .gguf weight files instead of the full-precision .safetensors checkpoints. That substitution is exactly where the failure modes concentrate: the loader has to find a .gguf file where it expects one, parse its metadata, and map it onto the right model class. When it cannot, you get a terse message that tells you almost nothing about which of those steps broke.

Symptom vs. Cause: A Quick Diagnosis Table

Before changing anything, match your exact symptom to the most likely cause. This is the fastest way to avoid shotgun uninstall-and-reinstall cycles that reset your working setup for no benefit.

• Symptom: Loader says “Failed to find the quantized version” even though you can see a .gguf file on disk. Likely cause: The file is in a directory the node does not scan, or you selected the wrong loader type (for example, a Unet-GGUF loader pointing at a text-encoder GGUF file).
• Symptom: The custom node will not even import at startup; you see ImportError: No module named 'gguf'. Likely cause: The Python gguf package is missing or too old for the node version.
• Symptom: Import succeeds but loading a valid .gguf throws a version or attribute error. Likely cause: A gguf package version mismatch between what produced the file and what your node reads.
• Symptom: Quantized weights load but the run dies with a CUDA error only on certain models. Likely cause: The quantization type or backend does not match your PyTorch CUDA build, or the model requires an attention or offload flag your launch command does not set.

The order of these symptoms mirrors the loader’s internal sequence: import, locate file, parse metadata, allocate tensors. Working through them in that order is the reliable path.

Step 1 — Verify the Dependency Chain First

Most “quantized version not found” reports that are not simply a wrong file path trace back to the gguf Python package. ComfyUI-GGUF reads GGUF files with this library, and the project’s own requirements list a floor version (for example, gguf>=0.13.0 in recent releases). An older copy can fail to parse newer files and produce a misleading “not found” rather than a clear “version too old”.

Check what you have installed and compare it against the node’s requirements.txt:

python -m pip show gguf
# and inside the ComfyUI-GGUF folder:
cat custom_nodes/ComfyUI-GGUF/requirements.txt

If the package is absent, install it inside the same Python environment that runs ComfyUI (not a different venv):

python -m pip install -r custom_nodes/ComfyUI-GGUF/requirements.txt

A very common trap is running the install in a base environment while ComfyUI runs from a venv or a portable (embedded) Python bundle. The node only sees libraries in the interpreter that actually launches main.py. When in doubt, invoke ComfyUI’s own pip rather than the system one.

Step 2 — Confirm the File Exists Where the Loader Looks

The “quantized version” message is, at its core, a lookup failure. The node checks specific model directories for a .gguf file matching the name you picked in the dropdown. Three things commonly break this lookup:

1. The file is a .safetensors checkpoint, not a .gguf, and you assumed the node would convert it automatically. It does not. You need a pre-quantized download (for example the FLUX.1 or SD3.5 GGUF variants hosted on Hugging Face).
2. The file is a GGUF but for a different component type. A text-encoder GGUF selected in a Unet loader (or vice versa) will not match the expected architecture and the node rejects it.
3. The file sits in a custom path not declared in extra_model_paths.yaml, so the node never scans it.

Verify the expected location and type explicitly. Confirm the file extension, the directory, and that the loader node’s type matches the model component you are actually loading (checkpoint vs. clip/text-encoder vs. VAE). This single alignment check resolves a large fraction of first-time failures.

Step 3 — Correct the Quantization and Backend Mismatches

Once the file is found, the next failure class is mismatched quantization types. Different GGUF quantizations (Q2_K through Q8_0, plus the newer IQ variants) have different memory footprints, quality trade-offs, and — critically — different compatibility with certain attention implementations. A file that quantized fine on the uploader’s machine can fail on yours if your PyTorch build or launch flags differ.

Check your launch command for flags that affect where tensors live and how attention runs. For low-VRAM setups, ComfyUI’s --lowvram and --normalvram flags change offload behavior, and --use-split-cross-attention or --use-pytorch-cross-attention change the attention backend. If a GGUF model loads into system RAM only or OOMs when attention spikes, adjusting these flags — not reinstalling the node — is usually the fix.

Also confirm your PyTorch build actually has CUDA. The AssertionError: Torch not compiled with CUDA enabled message appears when the quantized loader tries to move weights to a GPU that the installed PyTorch cannot see. Verify with:

python -c "import torch; print(torch.__version__); print(torch.cuda.is_available())"

If that prints False on a machine with an NVIDIA GPU, the PyTorch install (not the GGUF node) is the problem, and the fix is reinstalling the CUDA-enabled wheel from the official PyTorch index rather than the CPU-only default.

Common Pitfalls That Mask the Real Error

Several habits make these failures harder to diagnose than they need to be:

• Editing the node’s files by hand to “fix” a path. This works until the node updates and silently reverts, at which point the error returns with no obvious cause. Prefer extra_model_paths.yaml for path configuration.
• Cloning the node into a different folder name. ComfyUI registers custom nodes by folder, and renaming the folder can break the loader’s package-relative imports.
• Mixing GGUF versions across a workflow. A workflow saved against one node version can send parameters the newer loader no longer accepts, producing an error that looks like a file problem but is actually a schema drift.
• Ignoring the startup log. ComfyUI prints import warnings and node registration failures at launch. A node that fails to import often leaves a stale or broken entry that later manifests as “not found” errors on load.

Whenever the error is ambiguous, restart ComfyUI and read the first 20-30 lines of the console before loading anything. The import-stage messages contain the specific reason far more often than the in-workflow popup does.

How to Verify the Fix

A fix is only real if you can demonstrate the loader now reaches the parse-and-allocate stage. Use a deliberately small test rather than your full production workflow:

1. Load the GGUF model that previously failed, with an empty prompt and the smallest resolution your model supports.
2. Watch the console: you should now see metadata parsing (file size, quantization type, tensor count) instead of the “quantized version” message.
3. Run one generation to completion. A successful single image or token proves the dependency chain, file location, and backend are all aligned.
4. Only then re-import your real workflow, checking that the node types and parameter schema still match.

If the small test passes but the real workflow still fails, the problem is workflow-specific (a mismatched loader type or stale schema), not the node installation, and you can narrow the search accordingly.

Conclusion

The “Failed to find the quantized version” error in ComfyUI-GGUF is almost never a single fault. It is a symptom of a three-stage chain — dependency, file lookup, and backend alignment — where any one broken link produces the same terse message. Diagnosing in that order (verify the gguf package, confirm the file type and location, then correct quantization and CUDA mismatches) turns a frustrating black-box failure into a short, deterministic checklist. For deeper background on the surrounding tooling, see our piece on running open-source video models on consumer hardware and the specifics of motion-model memory tuning.

For current requirements and the exact list of supported quantizations, always consult the ComfyUI-GGUF repository, since version floors and supported file variants change with each release.