Hardware-aware AI Optimization. Handled by Experts.
Hardware-aware AI Optimization. Handled by Experts.
We optimize your models for any target device, proven by successful deployment across 100+ hardware environments.
We optimize your models for any target device, proven by successful deployment across 100+ hardware environments.
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We've made hundreds of models work across 100+ devices.
Now, we can make yours work too.
We've made hundreds of models work across 100+ devices.
Now, we can make yours work too.
What's stopping your model from running on your device?
What's stopping your model from running on your device?
100
+
DEVICES SUPPORTED
WHAT WE DO
Optimization Services Tailored to Your Deployment
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.
FROM PROBLEM TO DEPLOYMENT
Are You Experiencing These Challenges?
Hover over each challenge to see how we solve it.
01
Your customers bring new models - but you can’t support them.
02
You try to deploy - but the model won’t run on your device.
03
You have an NPU - but it’s not being used.
04
You deploy LLMs - but the cost keeps growing.
Proven Numbers in Real Deployment
18
X
Faster Inference
70
%
Memory reduction
50
%
Lower inference cost
99.6
%
NPU utilization
** Performance targets are jointly defined before project kickoff.
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
Entrust Your Optimization to Experts, So You Can Innovate with Confidence.
© 2022-2026. Nota Inc. All rights reserved.
© 2022-2026. Nota Inc. All rights reserved.
© 2022-2026. Nota Inc. All rights reserved.