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GGUF vs Safetensors: inference delivery and weight storage
GGUF delivers inference models to compatible GGML-based implementations. Safetensors stores named tensors; model definitions and execution environments are supplied separately.
Which format should you choose?
Consider GGUF for inference in a compatible GGML-based environment, or Safetensors for weights used by a compatible model implementation.
Good fit for GGUF
Storing and distributing inference models for compatible GGML runtimes · Keeping model information and tensors in a single format
- Local inference with compatible tools such as llama.cpp
- Distributing quantized or non-quantized models supported by the runtime
Good fit for Safetensors
Storing and distributing machine-learning tensor weights · Loading weights across supported frameworks
- Exchanging tensors and basic metadata rather than arbitrary Python objects
- Workflows that inspect and load selected tensors
Basic information
| Field | GGUF | Safetensors |
|---|---|---|
| Extension | .gguf | .safetensors |
| MIME type | application/octet-stream | application/octet-stream |
| Common uses | Storing and distributing inference models for compatible GGML runtimes · Keeping model information and tensors in a single format | Storing and distributing machine-learning tensor weights · Loading weights across supported frameworks |
| How to open | Open it with a trusted GGUF loader that supports the model and tensor types. Check the architecture, runtime version, quantization method, and whether shards or auxiliary files are required. | Use a Safetensors-compatible library or model tool. Supported tools can inspect tensor names, dtypes, and shapes before loading all weights to a device. The JSON header does not make the entire file a JSON text document. |
| Export or convert | Follow the conversion instructions for the source model and target runtime. Conversion and quantization may be separate steps. Evaluate quality, speed, and memory on representative inputs instead of choosing only by file size; keep all shards and required files such as a multimodal mmproj together. | Save tensor weights through an API supported by the model framework. Converting from a pickle-based input may deserialize that input, so do not load an untrusted source casually. Compare tensor names, shapes, dtypes, element counts, and model outputs after conversion. |
Choose by use case
Local inference with compatible tools such as llama.cpp
Storing and distributing inference models for compatible GGML runtimes · Keeping model information and tensors in a single format
GGUFDistributing quantized or non-quantized models supported by the runtime
Storing and distributing inference models for compatible GGML runtimes · Keeping model information and tensors in a single format
GGUFExchanging tensors and basic metadata rather than arbitrary Python objects
Storing and distributing machine-learning tensor weights · Loading weights across supported frameworks
SafetensorsWorkflows that inspect and load selected tensors
Storing and distributing machine-learning tensor weights · Loading weights across supported frameworks
SafetensorsKey point 1
GGUF includes key-value model metadata and tensor descriptors.
More on the differencesKey point 2
Safetensors records tensor names, dtype, shape and data ranges, not arbitrary inference code.
More on the differencesMore on the differences
Renaming an extension does not interchange these formats. Keep the source and choose converters and loaders supporting the architecture and tensor types.
- GGUF supports floating-point and quantized tensor types. Quantization can change values and is not merely lossless compression; the format name alone says nothing about size or precision.
- Check configuration, tokenizers and shard indexes for Safetensors, and split or auxiliary files for GGUF as required by the distribution. Compare outputs, memory and speed with identical inputs and preprocessing.
Frequently asked questions
Can GGUF fully restore the original Safetensors file?
Not guaranteed. If quantization changed values, conversion alone cannot recover original precision or training state. Retain the original weights and settings.