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Data as of Oct 18, 2026Based on 486 AI responses
Reviewed by Dimitry Apollonsky
AI summary
ONNX Runtime holds a narrow lead over NVIDIA TensorRT for hardware-versatile edge deployment and graph-based model quantization. When buyers look to distill large foundation models into smaller variants for on-device inference, Amazon Bedrock is the usual answer.
Rank and Mentioned in · since Jul 5
| # | Brand | Recommended for | Mentioned in |
|---|---|---|---|
| 1 | hardware-agnostic edge inference across heterogeneous CPU, GPU, and Arm devices | hardware-agnostic edge inference across heterogeneous CPU, GPU, and Arm devices | 35% |
| 2 | low-power model optimization and deployment for mobile and embedded devices | low-power model optimization and deployment for mobile and embedded devices | 28% |
| 3 | high-throughput post-training quantization and acceleration on NVIDIA edge hardware | high-throughput post-training quantization and acceleration on NVIDIA edge hardware | 28% |
| 4 | maximizing inference and weight compression across Intel CPUs and NPUs | maximizing inference and weight compression across Intel CPUs and NPUs | 25% |
| 5 | on-device inference and optimization specifically for PyTorch model workflows | on-device inference and optimization specifically for PyTorch model workflows | 18% |
| 6 | quantized local CPU inference using the GGUF ecosystem format | quantized local CPU inference using the GGUF ecosystem format | 17% |
| 7 | distilling large language models and open-source quantization toolchains | distilling large language models and open-source quantization toolchains | 15% |
| 8 | end-to-end TinyML quantization for microcontrollers and smart edge cameras | end-to-end TinyML quantization for microcontrollers and smart edge cameras | 15% |
| 9 | optimizing edge models for Android and automotive NPU hardware | optimizing edge models for Android and automotive NPU hardware | 14% |
| 10 | managed foundation model distillation from large teacher models to smaller students | managed foundation model distillation from large teacher models to smaller students | 12% |
| 11 | 12% | ||
| 12 | 10% | ||
| 13 | 10% | ||
| 14 | 9% | ||
| 15 | 9% |
What’s changing · since July
Since July, LegionEdgerising has become a common answer for foundation model distillation, and ExecuTorchrising has established itself among the most common frameworks for general edge ML deployment.
Weeks of Sep 21 – Oct 18, 2026 · Since Jul 5
Widest split: llama.cpp, 10% on ChatGPT Search and 25% on Google AI Mode.
| Brand | ChatGPT Search | Google AI Mode | Comparison |
|---|---|---|---|
| ONNX Runtime | 36% | 35% | |
| LiteRT | 27% | 30% | |
| NVIDIA TensorRT | 31% | 25% | |
| Neural Network Compression Framework | 25% | 24% | |
| ExecuTorch | 21% | 15% | |
| llama.cpp | 10% | 25% | |
| Hugging Face | 10% | 20% | |
| Edge Impulse | 9% | 21% | |
| Qualcomm AI Hub | 17% | 11% | |
| Amazon Bedrock | 13% | 12% |
40% of citations point at the ranked brands’ own sites. github.com, youtube.com, medium.com are the most-cited sites outside the ranked brands’ own domains.
Market map
Mentioned in · since Jul 5
Leading brands by topic
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We want to deploy an ML model to the edge. What is the best edge ML deployment framework?
I want to quantize my model to run faster and cheaper. What's the best model quantization library or toolkit?
My model's inference costs are skyrocketing on GPUs. Who specializes in model quantization and CPU-based inference optimization?
What is the best tool for debugging and visualizing the internal workings of a production Transformer model?
Could you suggest a framework for model distillation or quantization that helps turn a large, expensive model into a smaller, faster one?
I'm looking for a tool to perform model distillation or quantization. I need to convert a large, expensive model into something smaller and faster.
