GGUF and ONNX are the two most widely used model file formats for deploying language models outside of training infrastructure, but they serve different purposes and different deployment contexts. Choosing between them is not a quality decision — both can represent the same model at the same precision — it is a deployment decision that depends on your inference runtime, target hardware, and integration requirements. This guide explains what each format is, what it enables, and which to use for different deployment scenarios.
What GGUF Is
GGUF (GPT-Generated Unified Format) was created by the llama.cpp project as a successor to the earlier GGML format. It is a self-contained binary format that stores model weights, architecture metadata, tokeniser configuration, and quantisation parameters in a single file. The design goal is portability and simplicity: one file contains everything needed to load and run the model with any GGUF-compatible runtime. llama.cpp, Ollama, LM Studio, Jan AI, GPT4All, and most local AI applications on all major platforms use GGUF as their native format.
GGUF’s most important property is its quantisation support. The format natively encodes Q2, Q3, Q4, Q5, Q6, and Q8 quantisation variants with per-block scaling, enabling the compact K-quant formats (Q4_K_M, Q5_K_M, Q6_K) that dominate practical local deployment. A GGUF file runs efficiently on CPU using SIMD optimisations, on NVIDIA GPUs via CUDA, on AMD GPUs via ROCm or Vulkan, and on Apple Silicon via Metal — all from the same file, with the runtime selecting the appropriate compute path. This broad hardware support with a single file format is the core reason GGUF has become the community standard for local LLM deployment.
What ONNX Is
ONNX (Open Neural Network Exchange) is a standardised intermediate representation for neural network computation graphs, developed by Microsoft, Meta, and partners as an open standard for model interoperability. Where GGUF is purpose-built for LLM inference, ONNX is a general-purpose format covering all neural network architectures — vision models, speech models, embedding models, and language models alike. An ONNX file describes the model as a computation graph of standardised operators, which any ONNX-compatible runtime can execute.
ONNX Runtime (ORT) is Microsoft’s high-performance inference engine for ONNX models. It supports execution providers for different hardware: CPUExecutionProvider for CPU inference, CUDAExecutionProvider for NVIDIA GPUs, TensorRTExecutionProvider for NVIDIA TensorRT optimisation, DirectMLExecutionProvider for Windows GPU (AMD, Intel, NVIDIA), CoreMLExecutionProvider for Apple hardware, ROCmExecutionProvider for AMD GPUs, and others. This execution provider system is ONNX’s key strength for enterprise and cross-platform deployment: the same ONNX model file runs on all of these hardware targets through a unified API.
Figure 1 — GGUF vs ONNX: deployment scenario comparison
GGUF Strengths and Weaknesses
GGUF excels for local desktop and laptop deployment. Ollama, the most widely used local LLM tool, is built entirely around GGUF. LM Studio, Jan AI, and most graphical local AI applications use GGUF natively. The Hugging Face Hub hosts tens of thousands of GGUF models in multiple quantisation variants, ready to download without any conversion. llama.cpp’s CPU inference is highly optimised with AVX2, AVX-512, and ARM NEON SIMD instructions, delivering better CPU performance than ONNX Runtime for autoregressive LLM generation in most configurations.
GGUF’s weaknesses are its LLM-specific scope and its limited enterprise ecosystem. It is designed exclusively for transformer-based language models — it does not handle vision models, speech models, or embedding models with the same breadth as ONNX. Enterprise inference infrastructure built around ONNX Runtime, NVIDIA Triton, or Azure Machine Learning does not natively support GGUF, requiring conversion or a separate serving layer. For Windows deployments targeting Intel or AMD GPUs via DirectML, ONNX Runtime has better hardware support than llama.cpp.
ONNX Strengths and Weaknesses
ONNX’s primary strength is the breadth of its hardware execution provider ecosystem. DirectML support for Windows GPU inference (covering AMD, Intel, and NVIDIA through a single API) is a genuine advantage that GGUF lacks. Official Apple CoreML integration through ONNX Runtime is more mature than llama.cpp’s Metal backend for some model architectures. ONNX Runtime is deeply integrated into the HuggingFace Optimum library, which provides a simple export and inference path for any HuggingFace model. For Python applications that need to run the same model across different hardware with minimal code changes, ONNX Runtime’s execution provider abstraction is genuinely useful.
ONNX’s weakness for LLM deployment is the additional complexity of managing generation loops. ONNX models export as static computation graphs, but autoregressive LLM generation is inherently stateful — the KV cache grows with each generated token. Managing this in ONNX requires either re-exporting the model with explicit KV cache inputs and outputs (which the Optimum library handles, but adds complexity) or using a higher-level wrapper. For straightforward chat inference, this complexity makes ONNX more involved to set up than GGUF with llama.cpp or Ollama.
Embedding Models: ONNX Wins
For embedding models — sentence transformers, cross-encoders, classification models — ONNX is the better choice. The Hugging Face Optimum library exports any sentence-transformers model to ONNX in a single command, and ONNX Runtime inference for these encoder-only models is faster than using the full PyTorch model. The FastEmbed library (from Qdrant) uses ONNX Runtime under the hood for fast, dependency-light embedding inference without requiring PyTorch. For any application doing semantic search, RAG document embedding, or classification where the model is an encoder rather than a decoder, ONNX is the practical default.
Converting Between Formats
Converting from HuggingFace SafeTensors to GGUF uses the convert_hf_to_gguf.py script in the llama.cpp repository followed by the quantise tool. Converting to ONNX uses the HuggingFace Optimum library’s optimum-cli export onnx command, which handles architecture detection and export configuration automatically for supported model types. Converting from GGUF back to a HuggingFace format is possible but less well-supported — it is easier to start from the original SafeTensors model for any format conversion rather than starting from a GGUF file.
The Practical Decision
For local desktop and laptop LLM inference: GGUF with Ollama or llama.cpp. The ecosystem support, quantisation options, and ease of use are unmatched. For enterprise Windows deployment targeting multiple GPU vendors: ONNX with ONNX Runtime and DirectML. For mobile app deployment on iOS and Android: ONNX via CoreML and NNAPI execution providers. For Python applications embedding small models (classifiers, embedding models, cross-encoders) in a larger pipeline: ONNX with Optimum and FastEmbed. For most developers starting out, GGUF is the right default — it is what Ollama uses, what the Hub has the most of, and what the local AI community has built its tooling around. Switch to ONNX when a specific hardware target or enterprise integration requirement pushes you there.
SafeTensors: The Training Format
A third format worth mentioning is SafeTensors — Hugging Face’s format for storing model weights during training and fine-tuning. SafeTensors is not an inference format in the same sense as GGUF or ONNX: it stores raw float16 or float32 weights without quantisation or architecture metadata, and requires the transformers library and the model’s Python code to run. It is the format models are distributed in on the Hugging Face Hub before conversion. The practical workflow: models start as SafeTensors on the Hub, get converted to GGUF for local inference or ONNX for enterprise and mobile deployment. SafeTensors itself is not a deployment format choice — it is the starting point for conversion to either. When a model is available only in SafeTensors format and you need GGUF or ONNX, the conversion tools described above handle it from SafeTensors as the input.
Format Support on the Hugging Face Hub
The Hugging Face Hub’s format availability shapes practical deployment decisions. Most popular open models are available in all three formats from community contributors: the original SafeTensors from the model author, GGUF in multiple quantisation variants from TheBloke and other GGUF-focused contributors, and ONNX from the Optimum team or community converters. For smaller or newer models, GGUF availability is usually highest — the community converts to GGUF quickly — while ONNX conversion may lag by days or weeks after a model release. If you need a newly released model and ONNX is not yet available, GGUF with llama.cpp is the practical choice while waiting for the ONNX conversion to appear. Checking the Hub’s model card and the Files tab for available formats before starting a conversion yourself saves time on models that are already converted.
A Simple Decision Checklist
If your deployment runs on llama.cpp, Ollama, or LM Studio, choose GGUF. If it runs through ONNX Runtime, for example in a .NET application, on Windows with DirectML, or in a browser, choose ONNX. If you are unsure, convert the same model to both formats and benchmark them on your actual target hardware, measuring load time, memory use, and tokens per second, because the best format depends heavily on the runtime and device you ship to.