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NetsPresso Platform - See More →
NetsPresso Platform - See More →
NetsPresso Platform - See More →
I’d like todeploy 🧠Llama 3.3 70B🧠Gemma 4 E2B🧠Yolo11s🧠Llama 3.3 70Bon 🗄️NVIDIA A100📱Qualcomm SM8850🚙Renesas RZ/V2H🗄️NVIDIA A100with ⚡Memory Reduction 70%⚡40 TPS⚡40 TPS⚡Memory Reduction 70%
I’d like todeploy 🧠Llama 3.3 70B🧠Gemma 4 E2B🧠Yolo11s🧠Llama 3.3 70Bon 🗄️NVIDIA A100📱Qualcomm SM8850🚙Renesas RZ/V2H🗄️NVIDIA A100with ⚡Memory Reduction 70%⚡40 TPS⚡40 TPS⚡Memory Reduction 70%
Optimization starts with your goal.
NetPresso handles the rest.
TRUSTED BY LEADING ORGANIZATIONS
HOW NETSPRESSO WORKS
From Model to Deployment
Input your model, and NetsPresso transforms it into a high-performance, deployable model for your target hardware.
HOW NETSPRESSO WORKS
From Model to Deployment
Input your model, and NetsPresso transforms it into a high-performance, deployable model for your target hardware.
Real Numbers, Real Deployments
From GPU servers to automobiles, and robots —
optimized for wherever your model can run.
🎙️
61.3
X
Faster Inference
MODEL
Qwen3-ASR-0.6B
DEPLOYMENT
RTX 3090 GPU
RESULT
429ms → 7ms, 1-CER nearly preserved
🚗
10.9
X
Faster Automotive AI
MODEL
E2E model
DEPLOYMENT
Jetson AGX Thor
RESULT
1464ms → 134ms, PDMS -0.3%
🦾
1.63
X
Faster On-Device Robotics (VLA)
MODEL
SmolVLA 0.5B
DEPLOYMENT
Qualcomm IQ-9075 NPU
RESULT
505ms → 310ms, Success rate +4.6%
Solve Every Deployment Challenge
with One Platform
Turn deployment challenges into deployable results.
01
Not Running on Target Device
Architecture incompatibility blocks deployment
02
Unusable Performance
Models too slow for real-world use
03
Fragmented Workflow
Scattered toolchains create integration overhead
04
No Visibility Before Deployment
No way to validate performance before shipping
05
Rising Infrastructure Cost
GPU sprawl drives runaway inference expenses
All of these, solved by
A unified platform to deploy any AI model on any device — reliably, efficiently, at scale.
PROFESSIONAL SERVICE
Need Help? We've Got You Covered
When optimization becomes complex, our team ensures your models run successfully on your target device.
Edge AI Optimization
Expert-led model compression and hardware adaptation for edge devices including MCUs, mobile SoCs, and embedded platforms.
NPU Optimization
Deep compatibility work to make vision models and LLMs run on diverse NPU architectures with validated performance guarantees.
LLM Optimization
Specialize large language models for production — reduce GPU footprint, accelerate token throughput, and cut operational costs.
Customer Success Stories
Real problems. Real hardware. Real results.
AI FOUNDATION MODEL CONSORTIUM
"Deploying Massive LLMs at Half the Cost"
MoE-based LLM required excessive GPU resources and memory, making deployment economically unviable at scale.
50%
GPU Reduction
Memory Reduction
DEVICE MANUFACTURER
"Running AI on MCU with 125× Speed Improvement"
AI model could not run on MCU due to memory limits and software compatibility issues.
125x
Faster Inference
100%
Accuracy Preserved
SEMICONDUCTOR COMPANY
"Making CV Models Fully Deployable on NPU"
Multiple CV models were not compatible with target NPU, blocking product launch.
60%+
Size Reduction
✓
Real-Time Inference
DEVICE MANUFACTURER
"Achieving Real-Time Vision AI on Edge Devices"
Existing models too slow for real-time 1080p video processing on target hardware.
6x
Speed Improvement
✓
Real-Time 1080p
SEMICONDUCTOR COMPANY
"Making Large Vision-Language Models Deployable on NPU"
Model architecture was fundamentally incompatible with target NPU, preventing any deployment path.
18x
Faster Inference
↑
Improved Accuracy
AI FOUNDATION MODEL CONSORTIUM
"Deploying Massive LLMs at Half the Cost"
MoE-based LLM required excessive GPU resources and memory, making deployment economically unviable at scale.
50%
GPU Reduction
Memory Reduction
DEVICE MANUFACTURER
"Running AI on MCU with 125× Speed Improvement"
AI model could not run on MCU due to memory limits and software compatibility issues.
125x
Faster Inference
100%
Accuracy Preserved
SEMICONDUCTOR COMPANY
"Making CV Models Fully Deployable on NPU"
Multiple CV models were not compatible with target NPU, blocking product launch.
60%+
Size Reduction
✓
Real-Time Inference
DEVICE MANUFACTURER
"Achieving Real-Time Vision AI on Edge Devices"
Existing models too slow for real-time 1080p video processing on target hardware.
6x
Speed Improvement
✓
Real-Time 1080p
SEMICONDUCTOR COMPANY
"Making Large Vision-Language Models Deployable on NPU"
Model architecture was fundamentally incompatible with target NPU, preventing any deployment path.
18x
Faster Inference
↑
Improved Accuracy
AI FOUNDATION MODEL CONSORTIUM
"Deploying Massive LLMs at Half the Cost"
MoE-based LLM required excessive GPU resources and memory, making deployment economically unviable at scale.
50%
GPU Reduction
Memory Reduction
DEVICE MANUFACTURER
"Running AI on MCU with 125× Speed Improvement"
AI model could not run on MCU due to memory limits and software compatibility issues.
125x
Faster Inference
100%
Accuracy Preserved
SEMICONDUCTOR COMPANY
"Making CV Models Fully Deployable on NPU"
Multiple CV models were not compatible with target NPU, blocking product launch.
60%+
Size Reduction
✓
Real-Time Inference
DEVICE MANUFACTURER
"Achieving Real-Time Vision AI on Edge Devices"
Existing models too slow for real-time 1080p video processing on target hardware.
6x
Speed Improvement
✓
Real-Time 1080p
SEMICONDUCTOR COMPANY
"Making Large Vision-Language Models Deployable on NPU"
Model architecture was fundamentally incompatible with target NPU, preventing any deployment path.
18x
Faster Inference
↑
Improved Accuracy
AI FOUNDATION MODEL CONSORTIUM
"Deploying Massive LLMs at Half the Cost"
MoE-based LLM required excessive GPU resources and memory, making deployment economically unviable at scale.
50%
GPU Reduction
Memory Reduction
DEVICE MANUFACTURER
"Running AI on MCU with 125× Speed Improvement"
AI model could not run on MCU due to memory limits and software compatibility issues.
125x
Faster Inference
100%
Accuracy Preserved
SEMICONDUCTOR COMPANY
"Making CV Models Fully Deployable on NPU"
Multiple CV models were not compatible with target NPU, blocking product launch.
60%+
Size Reduction
✓
Real-Time Inference
DEVICE MANUFACTURER
"Achieving Real-Time Vision AI on Edge Devices"
Existing models too slow for real-time 1080p video processing on target hardware.
6x
Speed Improvement
✓
Real-Time 1080p
SEMICONDUCTOR COMPANY
"Making Large Vision-Language Models Deployable on NPU"
Model architecture was fundamentally incompatible with target NPU, preventing any deployment path.
18x
Faster Inference
↑
Improved Accuracy
AI FOUNDATION MODEL CONSORTIUM
"Deploying Massive LLMs at Half the Cost"
MoE-based LLM required excessive GPU resources and memory, making deployment economically unviable at scale.
50%
GPU Reduction
Memory Reduction
DEVICE MANUFACTURER
"Running AI on MCU with 125× Speed Improvement"
AI model could not run on MCU due to memory limits and software compatibility issues.
125x
Faster Inference
100%
Accuracy Preserved
SEMICONDUCTOR COMPANY
"Making CV Models Fully Deployable on NPU"
Multiple CV models were not compatible with target NPU, blocking product launch.
60%+
Size Reduction
✓
Real-Time Inference
DEVICE MANUFACTURER
"Achieving Real-Time Vision AI on Edge Devices"
Existing models too slow for real-time 1080p video processing on target hardware.
6x
Speed Improvement
✓
Real-Time 1080p
SEMICONDUCTOR COMPANY
"Making Large Vision-Language Models Deployable on NPU"
Model architecture was fundamentally incompatible with target NPU, preventing any deployment path.
18x
Faster Inference
↑
Improved Accuracy
AI FOUNDATION MODEL CONSORTIUM
"Deploying Massive LLMs at Half the Cost"
MoE-based LLM required excessive GPU resources and memory, making deployment economically unviable at scale.
50%
GPU Reduction
Memory Reduction
DEVICE MANUFACTURER
"Running AI on MCU with 125× Speed Improvement"
AI model could not run on MCU due to memory limits and software compatibility issues.
125x
Faster Inference
100%
Accuracy Preserved
SEMICONDUCTOR COMPANY
"Making CV Models Fully Deployable on NPU"
Multiple CV models were not compatible with target NPU, blocking product launch.
60%+
Size Reduction
✓
Real-Time Inference
DEVICE MANUFACTURER
"Achieving Real-Time Vision AI on Edge Devices"
Existing models too slow for real-time 1080p video processing on target hardware.
6x
Speed Improvement
✓
Real-Time 1080p
SEMICONDUCTOR COMPANY
"Making Large Vision-Language Models Deployable on NPU"
Model architecture was fundamentally incompatible with target NPU, preventing any deployment path.
18x
Faster Inference
↑
Improved Accuracy
AI FOUNDATION MODEL CONSORTIUM
"Deploying Massive LLMs at Half the Cost"
MoE-based LLM required excessive GPU resources and memory, making deployment economically unviable at scale.
50%
GPU Reduction
Memory Reduction
DEVICE MANUFACTURER
"Running AI on MCU with 125× Speed Improvement"
AI model could not run on MCU due to memory limits and software compatibility issues.
125x
Faster Inference
100%
Accuracy Preserved
SEMICONDUCTOR COMPANY
"Making CV Models Fully Deployable on NPU"
Multiple CV models were not compatible with target NPU, blocking product launch.
60%+
Size Reduction
✓
Real-Time Inference
DEVICE MANUFACTURER
"Achieving Real-Time Vision AI on Edge Devices"
Existing models too slow for real-time 1080p video processing on target hardware.
6x
Speed Improvement
✓
Real-Time 1080p
SEMICONDUCTOR COMPANY
"Making Large Vision-Language Models Deployable on NPU"
Model architecture was fundamentally incompatible with target NPU, preventing any deployment path.
18x
Faster Inference
↑
Improved Accuracy
AI FOUNDATION MODEL CONSORTIUM
"Deploying Massive LLMs at Half the Cost"
MoE-based LLM required excessive GPU resources and memory, making deployment economically unviable at scale.
50%
GPU Reduction
Memory Reduction
DEVICE MANUFACTURER
"Running AI on MCU with 125× Speed Improvement"
AI model could not run on MCU due to memory limits and software compatibility issues.
125x
Faster Inference
100%
Accuracy Preserved
SEMICONDUCTOR COMPANY
"Making CV Models Fully Deployable on NPU"
Multiple CV models were not compatible with target NPU, blocking product launch.
60%+
Size Reduction
✓
Real-Time Inference
DEVICE MANUFACTURER
"Achieving Real-Time Vision AI on Edge Devices"
Existing models too slow for real-time 1080p video processing on target hardware.
6x
Speed Improvement
✓
Real-Time 1080p
SEMICONDUCTOR COMPANY
"Making Large Vision-Language Models Deployable on NPU"
Model architecture was fundamentally incompatible with target NPU, preventing any deployment path.
18x
Faster Inference
↑
Improved Accuracy
AI FOUNDATION MODEL CONSORTIUM
"Deploying Massive LLMs at Half the Cost"
MoE-based LLM required excessive GPU resources and memory, making deployment economically unviable at scale.
50%
GPU Reduction
Memory Reduction
DEVICE MANUFACTURER
"Running AI on MCU with 125× Speed Improvement"
AI model could not run on MCU due to memory limits and software compatibility issues.
125x
Faster Inference
100%
Accuracy Preserved
SEMICONDUCTOR COMPANY
"Making CV Models Fully Deployable on NPU"
Multiple CV models were not compatible with target NPU, blocking product launch.
60%+
Size Reduction
✓
Real-Time Inference
DEVICE MANUFACTURER
"Achieving Real-Time Vision AI on Edge Devices"
Existing models too slow for real-time 1080p video processing on target hardware.
6x
Speed Improvement
✓
Real-Time 1080p
SEMICONDUCTOR COMPANY
"Making Large Vision-Language Models Deployable on NPU"
Model architecture was fundamentally incompatible with target NPU, preventing any deployment path.
18x
Faster Inference
↑
Improved Accuracy
AI FOUNDATION MODEL CONSORTIUM
"Deploying Massive LLMs at Half the Cost"
MoE-based LLM required excessive GPU resources and memory, making deployment economically unviable at scale.
50%
GPU Reduction
Memory Reduction
DEVICE MANUFACTURER
"Running AI on MCU with 125× Speed Improvement"
AI model could not run on MCU due to memory limits and software compatibility issues.
125x
Faster Inference
100%
Accuracy Preserved
SEMICONDUCTOR COMPANY
"Making CV Models Fully Deployable on NPU"
Multiple CV models were not compatible with target NPU, blocking product launch.
60%+
Size Reduction
✓
Real-Time Inference
DEVICE MANUFACTURER
"Achieving Real-Time Vision AI on Edge Devices"
Existing models too slow for real-time 1080p video processing on target hardware.
6x
Speed Improvement
✓
Real-Time 1080p
SEMICONDUCTOR COMPANY
"Making Large Vision-Language Models Deployable on NPU"
Model architecture was fundamentally incompatible with target NPU, preventing any deployment path.
18x
Faster Inference
↑
Improved Accuracy
Ready to make your model work on your device?
© 2022-2026. Nota Inc. All rights reserved.
© 2022-2026. Nota Inc. All rights reserved.
© 2022-2026. Nota Inc. All rights reserved.