Your LLM, anywhere

We provide professional services to port and optimize Gen AI models for mobile, IoT, and server environments.

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  • Qwen

  • TinyLlama

  • Llama

  • Gemma

  • LLaVA

  • Intern-VL

  • Qwen

  • TinyLlama

  • Llama

  • Gemma

  • LLaVA

  • Intern-VL

  • Qwen

  • TinyLlama

  • Llama

  • Gemma

  • LLaVA

  • Intern-VL

Taking Foundation Models to the Edge

You can now deploy any LLM or VLM, even those that were previously too large or complex.

  • Mobile

  • Smart home

  • Robotics

  • Automotive

  • Server

  • Industrial edge box

  • Mobile

  • Smart home

  • Robotics

  • Automotive

  • Server

  • Industrial edge box

  • Mobile

  • Smart home

  • Robotics

  • Automotive

  • Server

  • Industrial edge box

Optimized LLMs, Engineered with Hardware Insight

Specialized techniques meet real hardware knowledge to push Gen AI to its limits.

Bridging

AI and Hardware

Bridging

AI and Hardware

Bridging

AI and Hardware

We understand both model internals and hardware constraints — with extensive experience in deploying models across real-world devices.

We understand both model internals and hardware constraints — with extensive experience in deploying models across real-world devices.

We understand both model internals and hardware constraints — with extensive experience in deploying models across real-world devices.

Hardware-Aware Quantization

Hardware-Aware Quantization

Hardware-Aware Quantization

We apply device-specific quantization strategies to maximize performance while preserving accuracy.

We apply device-specific quantization strategies to maximize performance while preserving accuracy.

We apply device-specific quantization strategies to maximize performance while preserving accuracy.

Research-Driven

Optimization

Research-Driven

Optimization

Research-Driven

Optimization

We implement the latest methods from cutting-edge papers — including mixed precision quantization, operator fusion, and graph pruning.

We implement the latest methods from cutting-edge papers — including mixed precision quantization, operator fusion, and graph pruning.

We implement the latest methods from cutting-edge papers — including mixed precision quantization, operator fusion, and graph pruning.

Real Results.
Proven Performance.

The NetsPresso team enhanced LLM performance on real devices by combining quantization with hardware-aware techniques, leading to measurable gains in memory efficiency, speed, and output quality.

The NetsPresso team enhanced LLM performance on real devices by combining quantization with hardware-aware techniques, leading to measurable gains in memory efficiency, speed, and output quality.

The NetsPresso team enhanced LLM performance on real devices by combining quantization with hardware-aware techniques, leading to measurable gains in memory efficiency, speed, and output quality.

Optimized quantization to meet both efficiency and accuracy

Original model

Nota-optimized model

Memory Efficiency

Memory

(GB)

Reduced by

6.9%

Inference Speed

Prefill speed

(token/s)

1.27x

faster

Decoding speed

(token/s)

1.15x

faster

Text Generation Quality

Instruction

Understanding

Improved by

6.2%

Text

Summarization

Improved by

11.7%

· Llama 3-8B benchmark before and after optimization (under the same conditions)

· Measurements taken on Qualcomm DragonWing IQ-9075.

Llama, Qualcomm, and DragonWing IQ-9075 are used for illustrative purposes only and do not imply any affiliation with, endorsement by, or sponsorship from the respective third parties.

Performance enhancement through advanced decoding techniques

SS Decoding Off

SS Decoding On

Inference Speed

Decoding speed

(token/s)

10.9

1.78x

Faster

19.4

TTFT

(ms)

791

Almost

same

804

Applying self-speculative decoding to the llava1.5-7B.

The measurements were taken on the Qualcomm Snapdragon8 Gen3.

Quantization

Decoding

Quantization

Decoding

Optimized quantization to meet both efficiency and accuracy

Original model

Nota-optimized model

Inference Speed

Text Generation Quality

Memory Efficiency

Memory (GB)

Prefill speed (token/s)


1.27x

faster

Decoding speed (token/s)

1.15x

faster

Instruction Understanding

Improved by

6.2%

Text Summarization

Improved by

11.7%

· Llama 3-8B benchmark before and after optimization (under the same conditions)

· Measurements taken on Qualcomm DragonWing IQ-9075.

Llama, Qualcomm, and DragonWing IQ-9075 are used for illustrative purposes only and do not imply any affiliation with, endorsement by, or sponsorship from the respective third parties.

Reduced by

6.9%


Performance enhancement through advanced decoding techniques

SS Decoding On

Inference Speed

Decoding speed (token/s)

10.9

1.78x

Faster

19.4

TTFT (ms)

791

Almost

same

804

SS Decoding Off

Deep Optimization Backed by Research

Explore Our Research

LLMs that run anywhere and
know everything with RAG

LLMs that run anywhere and
know everything with RAG

Whether it’s standalone LLM or RAG-enhanced, we support both.

Whether it’s standalone LLM or RAG-enhanced, we support both.

Only LLM

Standalone language model without external data access

Standalone language model without external data access

Standalone language model without external data access

LLM

(Generate)

Question

Answer

Responses based on pre-trained knowledge only

Fast inference with lower system complexity

Answers are based only on pre-trained knowledge

Fast inference with lower system complexity

With RAG

With RAG

LLM connected to external knowledge sources

LLM connected to external knowledge sources

Retrieve
(Docs)

LLM

(Generate)

Answer

Question

Provides up-to-date and accurate information

Provides up-to-date and accurate information

Reduces hallucination by grounding in real data

Reduces hallucination by grounding in real data

Provides up-to-date and accurate information

Reduces hallucination by grounding in real data

RAG on Edge: Live Demo

Watch how our RAG-enabled LLM runs on a Qualcomm QCS6490 device — answering questions based on documents it hasn’t seen before.

Too Big, Too Slow, Too Costly?
Not Anymore.

The model is too large to deploy.

It’s slow and generates strange outputs.

High-end GPUs are too expensive.

We don’t know when development will finish.

Considering a product with generative AI.

It’s slow, and sometimes it generates really strange outputs.

It’s all good — the NetsPresso team will optimize it for smoother performance and a better user experience.

Ask me anything

Have a device in mind?
We’ll make your generative AI fit perfectly.

Talk to Our Experts

netspresso@nota.ai

© 2022-2026. Nota Inc. All rights reserved.

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Privacy policy

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이용약관

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개인정보처리방침

netspresso@nota.ai

© 2022-2026. Nota Inc. All rights reserved.

Terms of Service

|

Privacy policy

|

이용약관

|

개인정보처리방침

netspresso@nota.ai

© 2022-2026. Nota Inc. All rights reserved.

Terms of Service

|

Privacy policy

이용약관

|

개인정보처리방침