ONNX Runtime 2026
ONNX Runtime Tech Conference Model Optimization Cross-platform Inference Hardware Acceleration Edge Deployment LLM GenAI Quantization Production
| Feature | ONNX RT 1.x | ONNX RT 2.0 | Impact |
|---|---|---|---|
| LLM Support | Basic | PagedAttention KV-Cache | 3-5x faster LLM inference |
| Quantization | INT8 FP16 | INT4 AWQ GPTQ | 50-75% model size reduction |
| Web GPU | WASM only | WebGPU backend | 10x faster in browser |
| Mobile NPU | Limited | QNN Qualcomm NPU | Native NPU acceleration |
| GenAI Pipeline | Manual | Built-in text generation | Simplified LLM deployment |
| Olive Toolchain | Separate tool | Integrated optimization | One-click model optimization |
Model Conversion and Optimization
# === ONNX Model Export and Optimization ===
# PyTorch to ONNX
# import torch
# import onnx
#
# model = MyModel()
# model.load_state_dict(torch.load("model.pth"))
# model.eval()
#
# dummy_input = torch.randn(1, 3, 224, 224)
# torch.onnx.export(
# model, dummy_input, "model.onnx",
# input_names=["input"],
# output_names=["output"],
# dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}},
# opset_version=17,
# )
# Optimize with ONNX Runtime
# import onnxruntime as ort
# from onnxruntime.transformers import optimizer
#
# optimized = optimizer.optimize_model(
# "model.onnx",
# model_type="bert",
# num_heads=12,
# hidden_size=768,
# optimization_level=99,
# )
# optimized.save_model_to_file("model_optimized.onnx")
# Quantize INT8
# from onnxruntime.quantization import quantize_dynamic, QuantType
#
# quantize_dynamic(
# "model_optimized.onnx",
# "model_int8.onnx",
# weight_type=QuantType.QInt8,
# )
# Olive Toolchain (automated)
# olive run --config olive_config.json
# {
# "input_model": {"type": "PyTorchModel", "model_path": "model.pth"},
# "systems": {"local": {"type": "LocalSystem", "accelerators": [{"device": "gpu"}]}},
# "passes": {
# "onnx_conversion": {"type": "OnnxConversion", "opset": 17},
# "transformer_opt": {"type": "OrtTransformersOptimization", "model_type": "bert"},
# "quantization": {"type": "OnnxDynamicQuantization", "weight_type": "QInt8"}
# }
# }
from dataclasses import dataclass
@dataclass
class ModelBenchmark:
model: str
original_size: str
optimized_size: str
latency_original: str
latency_optimized: str
accuracy_drop: str
benchmarks = [
ModelBenchmark("BERT-base", "438 MB", "110 MB (INT8)", "12ms", "4ms", "< 0.5%"),
ModelBenchmark("ResNet-50", "98 MB", "25 MB (INT8)", "8ms", "2ms", "< 0.3%"),
ModelBenchmark("GPT-2 Small", "548 MB", "137 MB (INT8)", "45ms", "15ms", "< 1%"),
ModelBenchmark("Whisper Small", "967 MB", "242 MB (INT8)", "800ms", "250ms", "< 0.5%"),
ModelBenchmark("LLaMA 7B", "13 GB", "3.5 GB (INT4)", "2000ms", "500ms", "< 2%"),
]
print("=== Model Benchmarks ===")
for b in benchmarks:
print(f" [{b.model}] Size: {b.original_size} → {b.optimized_size}")
print(f" Latency: {b.latency_original} → {b.latency_optimized}")
print(f" Accuracy drop: {b.accuracy_drop}")Cross-platform Inference
# === ONNX Runtime on Multiple Platforms ===
# Python Inference
# import onnxruntime as ort
# import numpy as np
#
# providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
# session = ort.InferenceSession("model.onnx", providers=providers)
#
# input_name = session.get_inputs()[0].name
# result = session.run(None, {input_name: np.random.randn(1, 3, 224, 224).astype(np.float32)})
# C++ Inference
# #include
# Ort::Env env(ORT_LOGGING_LEVEL_WARNING, "inference");
# Ort::SessionOptions options;
# options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);
# Ort::Session session(env, "model.onnx", options);
# JavaScript (Web)
# import * as ort from 'onnxruntime-web';
# const session = await ort.InferenceSession.create('model.onnx',
# { executionProviders: ['webgpu', 'wasm'] });
# const feeds = { input: new ort.Tensor('float32', inputData, [1, 3, 224, 224]) };
# const results = await session.run(feeds);
# Mobile (React Native)
# import { InferenceSession } from 'onnxruntime-react-native';
# const session = await InferenceSession.create(modelPath);
@dataclass
class PlatformSupport:
platform: str
package: str
providers: str
use_case: str
platforms = [
PlatformSupport("Python", "onnxruntime / onnxruntime-gpu", "CUDA TensorRT OpenVINO DirectML", "Server inference, batch processing"),
PlatformSupport("C/C++", "onnxruntime C API", "CUDA TensorRT CoreML", "Embedded, high-performance"),
PlatformSupport("JavaScript Web", "onnxruntime-web", "WebGPU WASM", "Browser-based inference"),
PlatformSupport("Node.js", "onnxruntime-node", "CPU CUDA", "Server-side JS"),
PlatformSupport("iOS", "onnxruntime-objc", "CoreML Neural Engine", "Mobile AI apps"),
PlatformSupport("Android", "onnxruntime-android", "NNAPI QNN", "Mobile AI apps"),
PlatformSupport("React Native", "onnxruntime-react-native", "CoreML NNAPI", "Cross-platform mobile"),
]
print("\n=== Platform Support ===")
for p in platforms:
print(f" [{p.platform}] Package: {p.package}")
print(f" Providers: {p.providers}")
print(f" Use case: {p.use_case}") Production Deployment
# === Production Deployment Patterns ===
@dataclass
class DeployPattern:
pattern: str
architecture: str
latency: str
throughput: str
best_for: str
patterns = [
DeployPattern("REST API (FastAPI)", "FastAPI + ONNX Runtime + CUDA",
"5-20ms", "100-500 req/s per GPU", "Standard ML API"),
DeployPattern("gRPC (Triton)", "Triton Inference Server + ONNX",
"2-10ms", "500-5000 req/s per GPU", "High-throughput"),
DeployPattern("Batch Processing", "Spark + ONNX Runtime",
"Minutes", "Millions/batch", "Offline scoring"),
DeployPattern("Edge (Mobile)", "ONNX Runtime Mobile + INT8",
"10-50ms", "20-60 fps", "On-device inference"),
DeployPattern("Browser (WebGPU)", "onnxruntime-web + WebGPU",
"20-100ms", "10-30 fps", "Client-side AI"),
DeployPattern("Serverless", "AWS Lambda + ONNX Runtime",
"50-500ms (cold start)", "Concurrent scaling", "Variable traffic"),
]
print("Deployment Patterns:")
for d in patterns:
print(f" [{d.pattern}]")
print(f" Arch: {d.architecture}")
print(f" Latency: {d.latency} | Throughput: {d.throughput}")
print(f" Best for: {d.best_for}")
# Monitoring checklist
monitoring = {
"Latency p50/p95/p99": "Track percentile latency per endpoint",
"Throughput": "Requests per second per instance",
"GPU Utilization": "Target 60-80% for cost efficiency",
"Memory Usage": "Track GPU and CPU memory",
"Error Rate": "< 0.1% target",
"Model Version": "Track which version is serving",
"Accuracy Drift": "Compare predictions vs actuals weekly",
}
print(f"\n\nMonitoring Checklist:")
for k, v in monitoring.items():
print(f" [{k}]: {v}")เคล็ดลับ
- Olive: ใช้ Olive Toolchain Optimize อัตโนมัติ ไม่ต้องทำมือ
- INT8: เริ่มด้วย INT8 Quantization ได้ 3-4x speedup แทบไม่เสีย Accuracy
- Dynamic: ใช้ Dynamic Axes สำหรับ Variable Batch Size
- Provider: ทดสอบทุก Execution Provider เลือกที่เร็วสุดบน Hardware
- Profile: ใช้ ONNX Runtime Profiler หาจุด Bottleneck
ONNX Runtime คืออะไร
Inference Engine ONNX Format PyTorch TensorFlow Cross-platform Windows Linux macOS iOS Android Web CUDA TensorRT DirectML OpenVINO Open Source Microsoft
Tech Conference 2026 มีอะไรใหม่
ONNX RT 2.0 LLM PagedAttention KV-Cache INT4 AWQ GPTQ GenAI WebGPU QNN Qualcomm NPU Olive Toolchain Training Edge
Model Optimization ทำอย่างไร
Olive Toolchain Quantization INT8 INT4 FP16 Graph Optimization Operator Fusion Constant Folding TensorRT CUDA OpenVINO Transformer Optimizer Accuracy
Deploy บน Edge อย่างไร
torch.onnx.export Olive Quantize INT8 INT4 Mobile iOS Android Web Browser Embedded IoT Latency Memory Footprint Accuracy A/B Testing
สรุป
ONNX Runtime Tech Conference 2026 Model Optimization Olive Quantization INT8 INT4 Cross-platform CUDA WebGPU Mobile Edge LLM GenAI Production Deployment
