Platform

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Platform

Talk to an Expert

Platform

Talk to an Expert

NetsPresso

The Model Optimization Platform

End-to-end optimization pipeline for your hardware,
built for on-prem environments

Request Private Demo

WORKFLOW

One Command. Full Optimization.

Run the entire optimization pipeline from model converting and graph optimization to quantization with a single command

$ np run |

01

Advanced Quantization

02

Graph Optimization

03

Graph Quantization

Advanced Quantization (AQ)

AQ delivers SOTA quantization in Torch eager mode, integrating cutting-edge techniques into our stack for peak model efficiency - especially effective for LLMs.

Key Techniques

AWQ — Activation-based weight channel protection

AutoRound — Iterative optimization for higher precision

QuaRot — Rotation-based outlier suppression

RTN — Baseline round-to-nearest method

👉 Maximize compression while preserving accuracy in LLMs

Graph Optimization (GO)

Rewrites the computational graph to improve compatibility and efficiency without changing model parameters.

Key Techniques

Conv + BatchNorm fusion

QKV attention fusion

Remove unnecessary reshape / transpose

Dropout elimination

👉 From incompatible to hardware-ready

Graph Quantization (GQ)

Uses calibration data to determine optimal activation ranges and applies precision schemes across layers.

Key Techniques

W8-A8 — Balanced performance (default)

W4-A8 — Smaller model size

W4-A16 — Compressed weights, higher precision

W8-A16 — Accuracy-focused configuration

👉 Convert models into efficient formats

01

Advanced Quantization

02

Graph Optimization

03

Graph Quantization

Advanced Quantization (AQ)

AQ delivers SOTA quantization in Torch eager mode, integrating cutting-edge techniques into our stack for peak model efficiency - especially effective for LLMs.

Key Techniques

AWQ — Activation-based weight channel protection

AutoRound — Iterative optimization for higher precision

QuaRot — Rotation-based outlier suppression

RTN — Baseline round-to-nearest method

👉 Maximize compression while preserving accuracy in LLMs

Graph Optimization (GO)

Rewrites the computational graph to improve compatibility and efficiency without changing model parameters.

Key Techniques

Conv + BatchNorm fusion

QKV attention fusion

Remove unnecessary reshape / transpose

Dropout elimination

👉 From incompatible to hardware-ready

Graph Quantization (GQ)

Uses calibration data to determine optimal activation ranges and applies precision schemes across layers.

Key Techniques

W8-A8 — Balanced performance (default)

W4-A8 — Smaller model size

W4-A16 — Compressed weights, higher precision

W8-A16 — Accuracy-focused configuration

👉 Convert models into efficient formats

CORE CAPABILITIES

Everything You Need to Optimize and Deploy AI

Integrated Optimization Workflow
Run full optimization with minimal effort
Flexible Across Models & Hardware
Supports multiple model frameworks and hardwares
Deployment-Ready Model Zoo
Optimized models that actually run on target devices
One Workflow, Two Interfaces
CLI for automation, GUI for analysis

Integrated Optimization Workflow
Run full optimization with minimal effort
Flexible Across Models & Hardware
Supports multiple model frameworks and hardwares
Deployment-Ready Model Zoo
Optimized models that actually run on target devices
One Workflow, Two Interfaces
CLI for automation, GUI for analysis

Integrated Optimization Workflow
Run full optimization with minimal effort
Flexible Across Models & Hardware
Supports multiple model frameworks and hardwares
Deployment-Ready Model Zoo
Optimized models that actually run on target devices
One Workflow, Two Interfaces
CLI for automation, GUI for analysis

Real Results on Real Hardware

On device benchmarks:
Before vs. After optimization

Arm

NVIDIA

Alif Ensemble® E8

MODEL_ SiNet Segmentation

Graph Optimization via NetsPresso fully migrated the model to the NPU — eliminating CPU fallback and achieving near-complete hardware utilization.

LATENCY

57.9

781.8ms

RESULT

13.5x faster inference

CPU USAGE

0.4

46.9%

RESULT

478 → 3 nodes

NPU USAGE

99.6

53.1%

RESULT

542 → 784 nodes

NVIDIA B100/B200/B300

MODEL_ Solar-Open-100B(LLM, MoE, NVFP4)

Dramatic memory reduction from 191 GB to 58 GB — while successfully defending accuracy against competing methods.

WEIGHT MEMORY

58.7

191.2GB

RESULT

63.9% reduction (best-in-class accuracy, outperforms AutoRound)

MMLU-PRO

62.53

AutoRound 61.56

RESULT

Outperforms AutoRound

GENERAL BENCH

73.94

AutoRound 73.74

RESULT

Best-in-class accuracy

Arm

NVIDIA

Alif Ensemble® E8

MODEL_ SiNet Segmentation

Graph Optimization via NetsPresso fully migrated the model to the NPU — eliminating CPU fallback and achieving near-complete hardware utilization.

LATENCY

57.9

781.8ms

RESULT

13.5x faster inference

CPU USAGE

0.4

46.9%

RESULT

478 → 3 nodes

NPU USAGE

99.6

53.1%

RESULT

542 → 784 nodes

NVIDIA B100/B200/B300

MODEL_ Solar-Open-100B(LLM, MoE, NVFP4)

Dramatic memory reduction from 191 GB to 58 GB — while successfully defending accuracy against competing methods.

WEIGHT MEMORY

58.7

191.2GB

RESULT

63.9% reduction (best-in-class accuracy, outperforms AutoRound)

MMLU-PRO

62.53

AutoRound 61.56

RESULT

Outperforms AutoRound

GENERAL BENCH

73.94

AutoRound 73.74

RESULT

Best-in-class accuracy

Inside the Demo

Experience the full NetsPresso optimization pipeline in a real environment and validate performance improvements firsthand.

✓

No setup required

✓

Hands-on CLI-based optimization workflow

✓

Real benchmarks measured on target hardware

Request Private Demo

terminal — np run

Visualize and Compare Model Changes Across Every Iteration

A high-performance graph visualizer for instant topology comparison. No installation, 100% free.

Go to NetsPresso Probe

Model Diff

View topology changes between two models side-by-side, including new or removed nodes

Synchronized Graph Navigation

pan and zoom both graph views simultaneously

Custom Node Coloring

highlight the nodes that matter most to your team

Experience our optimization performance firsthand.

Request Private Demo

netspresso@nota.ai

이용약관

개인정보처리방침

netspresso@nota.ai

이용약관

개인정보처리방침

netspresso@nota.ai

이용약관

개인정보처리방침

NetsPresso

The Model Optimization Platform

End-to-end optimization pipeline for your hardware, built for on-prem environments

One Command. Full Optimization.

Run the entire optimization pipeline from model converting and graph optimization to quantization with a single command

01

Advanced Quantization

02

Graph Optimization

03

Graph Quantization

Advanced Quantization (AQ)

Graph Optimization (GO)

Graph Quantization (GQ)

01

Advanced Quantization

02

Graph Optimization

03

Graph Quantization

Advanced Quantization (AQ)

Graph Optimization (GO)

Graph Quantization (GQ)

Everything You Need to Optimize and Deploy AI

Real Results on Real Hardware

On device benchmarks:Before vs. After optimization

Inside the Demo

Experience the full NetsPresso optimization pipeline in a real environment and validate performance improvements firsthand.

Visualize and Compare Model Changes Across Every Iteration

A high-performance graph visualizer for instant topology comparison. No installation, 100% free.

Experience our optimization performance firsthand.

End-to-end optimization pipeline for your hardware,
built for on-prem environments

On device benchmarks:
Before vs. After optimization