Olive vs optimum

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What are the differences and what are the use cases for Olive versus optimum conversion of models to onnx format?

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for now:

Background: “ONNX conversion” is usually more than exporting a .onnx file

ONNX is a model graph format (exchange format), not the runtime. After export, you typically run it with ONNX Runtime (ORT) and an execution provider (CPU, CUDA, TensorRT, DirectML, OpenVINO, WebGPU/WASM, etc.). (Hugging Face)

So “convert to ONNX” often includes a chain of steps:

  • Export (PyTorch → ONNX graph)
  • Graph shaping (dynamic axes, input/output naming, multi-file graphs for encoder/decoder)
  • Transformer-specific graph fusions (LayerNorm, attention fusions, etc.)
  • Precision changes (FP32→FP16/BF16)
  • Quantization (INT8, etc.)
  • Target tuning (settings optimized for a specific backend + hardware)

Optimum and Olive both touch ONNX conversion, but they are optimized for different goals.

What Optimum is (in ONNX terms)

Optimum ONNX is Hugging Face’s Transformers-first exporter + ORT integration layer.

Core focus

  • Export Hugging Face models to ONNX in a task/architecture-aware way. (Hugging Face)
  • Make ONNX inference feel like Transformers by providing ORTModelFor* classes and integration patterns. (Hugging Face)

What Optimum does well

  • Correct exports for common HF tasks without hand-wiring graphs.
  • Generation exports with KV-cache (“with past”) for efficient token-by-token decoding (e.g., text-generation-with-past, text2text-generation-with-past). (Hugging Face)
  • A “safe” export path: the recommended main_export flow chooses the correct exporter, validates the exported model, and can run export-time optimizations. (Hugging Face)

Typical Optimum outcome

You get ONNX artifacts that are easy to load and run using Optimum’s ORT wrappers (and often easy to plug into existing HF-style code). (Hugging Face)

What Olive is (in ONNX terms)

Olive is Microsoft’s hardware-aware optimization workflow toolchain for ONNX Runtime.

Core focus

  • Treat “conversion” as an end-to-end deployment optimization pipeline: conversion + optimization + quantization + tuning, aimed at a specific target (hardware + execution provider). (ONNX Runtime)
  • Model optimization is expressed as a sequence of passes, each with tunable parameters; Olive can auto-tune passes using a search strategy and evaluators (latency/accuracy, plus custom metrics). (Microsoft GitHub)

Olive’s ONNX conversion options (important difference)

Olive can export/convert via different passes, including:

  • OnnxConversion (generic PyTorch → ONNX export) (Microsoft GitHub)
  • OptimumConversion (delegate export to Optimum’s HF-aware exporter) (Hugging Face)

Offline transformer optimizations are first-class

Olive includes a transformer optimization pass that can apply graph fusions offline in scenarios where ORT may not apply the newest transformations automatically at load time. (Microsoft GitHub)

Typical Olive outcome

You get an ONNX model (often plus additional artifacts/config) that is tailored to a deployment target, frequently involving quantization/precision changes and transformer-specific graph rewrites. (ONNX Runtime)

Key differences that matter in practice

1) Primary goal

  • Optimum: “Export HF models correctly and run them easily with ORT using HF-like APIs.” (Hugging Face)
  • Olive: “Produce the best-performing deployable model for a given hardware target, using workflows, tuning, and evaluation.” (ONNX Runtime)

2) Where task/architecture knowledge lives

  • Optimum: built around HF tasks; knows about export variants like with-past and common multi-graph layouts. (Hugging Face)
  • Olive: task knowledge is “pluggable”: you either use generic conversion or you choose passes that incorporate task-aware exporters (e.g., OptimumConversion). (Microsoft GitHub)

3) Optimization philosophy

  • Optimum: generally offers convenient switches/presets at export time (and HF-friendly post steps). (Hugging Face)
  • Olive: is designed for systematic tuning—passes + parameters + evaluation + search strategy. (Microsoft GitHub)

4) Hardware targeting as a first-class concept

  • Optimum: works broadly, but it’s not primarily a “target hardware optimizer.”
  • Olive: explicitly markets hardware-aware optimization and is heavily used in DirectML/Windows acceleration narratives (including large perf claims after Olive optimization). (Microsoft for Developers)

Use cases: when to pick which

Choose Optimum when…

  1. You’re starting from Hugging Face Transformers and want the most reliable ONNX export with minimal glue. (Hugging Face)
  2. You need generation-ready exports (KV-cache “with past”) without manually managing cache tensors and graph splits. (Hugging Face)
  3. Your deployment code wants an HF-like interface (ORTModel wrappers). (Hugging Face)
  4. You want the simplest “export → validate → run” workflow (especially for encoder-only and many common seq2seq/generation tasks). (Hugging Face)

Typical examples

  • BERT-like classifiers/embedders to ORT for CPU/GPU inference
  • Encoder–decoder summarization/translation exports that produce multiple ONNX files in a predictable layout (Hugging Face)
  • Decoder-only generation that must be efficient (with-past) (Hugging Face)

Choose Olive when…

  1. Your real objective is best latency/throughput on a specific target (e.g., DirectML, edge, vendor accelerators), and you can measure it. (ONNX Runtime)
  2. You want an orchestrated pipeline that chains conversion + transformer graph fusions + precision changes + quantization, with tuning and evaluation. (ONNX Runtime)
  3. You are deploying in a context where Olive is already “part of the stack” (DirectML/Windows AI PC guidance and examples). (Microsoft for Developers)

Typical examples

  • “Make this model fast on Windows via DirectML”
  • “Quantize and optimize under an accuracy constraint”
  • “Automate exploring multiple quantization/optimization strategies”

Choose both (very common) when…

You need Optimum’s export correctness/coverage and Olive’s hardware-aware optimization workflow.

A concrete documented pattern is:

  1. OptimumConversion, 2) OrtTransformersOptimization, 3) FP32→FP16, etc. (Hugging Face)

This is also the low-regret “industrial” approach: get a correct baseline export first, then optimize for targets.

Practical decision rule

If your question is mainly…

  • “How do I export this HF model to ONNX correctly?” → Optimum first. (Hugging Face)
  • “How do I maximize performance on this hardware / execution provider?” → Olive (often using OptimumConversion). (ONNX Runtime)
  • “I need both correctness and target performance” → Optimum export → Olive optimize/tune. (Hugging Face)

Common pitfalls and how they map to each tool

Pitfall: exporting the wrong generation variant

If you export text-generation instead of text-generation-with-past, you may get an ONNX model that works but is slow for autoregressive decoding. Optimum makes this distinction explicit in its task flags. (Hugging Face)

Pitfall: expecting Olive to “just quantize” without configuring evaluation/workflow

Olive is workflow-driven; if your pass chain or evaluator isn’t set up as intended, you can end up with a large unquantized artifact (this shows up in real Olive usage discussions/issues). (ONNX Runtime)

Pitfall: assuming ORT will apply every transformer optimization automatically

ORT applies many optimizations at load time, but the transformer optimization tool and Olive’s offline pass exist because some optimizations may need to be applied offline or are newer than what ORT applies by default. (Microsoft GitHub)

Summary in one sentence

  • Optimum is the HF-native ONNX export + ORT integration path;
  • Olive is the hardware-aware ONNX Runtime optimization workflow path—often wrapping Optimum export when that yields better task-aware conversion. (Hugging Face)