Edge AI · NPU Optimization

AI, Native to
Every Machine.

Optimize and deploy AI across cloud, edge, and any compute architecture.

Kernative AI takes cloud-scale models and makes them run — fast, private, and offline — on the NPU already inside your product.

Talk to Engineering See the Stack ↓
“Silicon-native optimization, not generic compression.”
85%+NPU Utilization
87msFirst-token Latency
<1%Accuracy Loss
100%Offline Capable
AI model data converging onto an NPU chip mounted on a circuit board
Company

Engineers who've spent years
inside the silicon.

Kernative Technology Inc. is a US-headquartered edge AI company built around a core team with backgrounds at Qualcomm and AMD — engineers who spent years working inside NPU silicon, not just calling APIs on top of it.

We believe the future of AI is not in the cloud — it's on the device. Our mission is to make every NPU, from an automotive SoC to an AI PC, run models at peak efficiency, with minimal accuracy loss and maximum performance per watt.

“The quantization layer for the next billion AI devices.”
“Silicon-native optimization, not generic compression.”
Kernative AI's California headquarters, a modern glass office building with desert landscaping
Technology

One team, one contract,
end-to-end delivery.

We provide a complete technology stack from silicon to application — not a research paper, not a point solution.

Application LayerVoice Assistant · Driver Monitor · Smart Cockpit · AR/VR AI · Industrial AI
Model LayerLlama · Qwen · Mistral · Custom Models · Knowledge Distillation
Optimization LayerINT8 / INT4 Quantization · Structured Pruning · Layer Fusion · Memory Layout
Runtime LayerHexagon NPU Kernel · ONNX Runtime · TensorRT · CoreML
Hardware LayerQualcomm Snapdragon · AMD Ryzen AI · Apple Neural Engine · NVIDIA Jetson
What Makes Our Stack Different
LayerWhat Others DoWhat We Do
HardwareSupport one chipQualcomm · AMD
RuntimeGeneric ONNXHexagon-native kernel optimization
OptimizationPost-training quantizationHardware-aware INT8/INT4 + pruning + distillation
ModelLlama onlyLlama · Qwen · Mistral · Gemini Nano · Custom
ApplicationOne verticalAuto · Industrial · Consumer · Robotics
Diagram showing a cloud model quantized by Kernative and deployed onto an on-device NPU, from cloud model through Kernative quantization to on-device NPU
Capabilities

Three pillars, one competitive position.

Model Quantization

INT8/INT4 precision, structured pruning, knowledge distillation, mixed-precision per layer.
→ 50%+ latency reduction · <1% accuracy loss · 7B model in <7 days

NPU Optimization

Hardware-specific kernel tuning, layer fusion, memory layout optimization for Hexagon, Ryzen AI, and Neural Engine.
→ 85%+ NPU utilization · 2× performance per watt

On-Device Inference

100% offline runtime, zero data transmission, sub-100ms latency on automotive-grade NPU.
→ Full data privacy · always works · no API costs
Competitive Positioning

Purpose-built advantage for on-device AI

Kernative compared with the industry average across six critical deployment dimensions.

NPU Utilization Latency Reduction Time to Deploy Chip Specificity Data Privacy Offline Capability
Kernative Industry Average
KERNATIVE AI
Benchmarks

Performance at a glance.

MetricIndustry AverageKernative AIImprovement
First-token Latency200–500ms (cloud)87ms (on-device)4–6x faster
NPU Utilization30–50%85%+2x higher
Offline CapabilityRequires connection100% offlineAlways works
Data PrivacyCloud roundtripZero data leaves deviceFully private
Cost ModelPer-API-call (ongoing)One-time deploymentCapEx vs. OpEx
CustomizationGeneric modelSilicon-specific tuningFully tuned
Accuracy Loss2–5%<1%3–5x better
Quantization Speed1–2 weeks<7 days (7B model)2x faster

vs. Cloud-only AI Solutions

  • 5–10x lower latency — no network roundtrip, no server queue
  • Zero ongoing API costs — pay once, run forever
  • Full data sovereignty — sensitive data never leaves the device
  • Works without internet — truly edge, truly reliable

vs. Generic Quantization Tools

  • Hardware-aware quantization — tuned for Qualcomm Hexagon, not just "any NPU"
  • Accuracy-preserving — <1% loss, not the 2–5% typical of off-the-shelf tools
  • Production-ready — deployed in real products, not research demos
  • Multi-model support — Llama, Qwen, Mistral, Gemini Nano, and custom architectures
Products

Two products,
one platform.

A compression toolchain for teams shipping their own models, and a ready-to-run edge AI box for teams who'd rather not build silicon expertise in-house.

Product Line 01 · Software

Kernative Studio

A self-built toolchain that takes any mainstream model from checkpoint to a validated, deploy-ready package — quantize, optimize, compile, and integrate, in one visual pipeline.

Kernative Studio interface showing the compression pipeline, model graph, performance chart, and deployment targets
Kernative Studio · Compress workflow, model graph & deployment targets
Kernative Compress

Low-bit quantization (INT8/INT4/INT2 mixed precision), structured pruning, knowledge distillation, and KV-cache compression.

Kernative Inference

Heterogeneous NPU/CPU/GPU scheduling, operator-level kernel optimization, speculative decoding, dynamic batching.

Kernative Compiler

Graph lowering and hardware-specific kernel codegen — bridges compressed models to each target silicon backend.

Kernative OneSDK

One integration surface for chat, multimodal interaction, vision detection, industrial inspection, and edge AIoT.

Product Line 02 · Hardware

Kernative AIBox

A chip-integrated edge AI box — power it on and a 3B–8B model is already running locally. Available as a complete enclosure or a core-board module for custom integration.

Kernative AIBox enclosure on a lab bench, showing USB, Ethernet, and power ports
Kernative AIBox · complete enclosure, ready to deploy
Platform 01

iBox‑R

  • Edge NPU coprocessor · 20 TOPS INT8
  • 5 GB on-package 3D-stacked DRAM · ~1 TB/s theoretical bandwidth
  • Multi-core embedded CPU
  • Runs 3B / 7B models locally · 100+ tokens/sec output
Best for: production-line inspection, industrial monitoring, edge AIoT, in-cabin systems.
Platform 02

iBox‑G

  • 3D-stacked near-memory compute platform
  • ~10× effective bandwidth vs. traditional designs · ~1/3 the power draw
  • Streaming compute architecture tuned for transformer FFN workloads
  • Demonstrated 300 tokens/sec on an 8B model, single chip
Best for: offline conversational AI, multimodal streaming, privacy-sensitive deployments (retail, healthcare).

Both platforms ship preloaded with the Kernative dual engine (Compress + Inference) and OneSDK — offline-capable out of the box. Chip performance figures per silicon partner public disclosures; final configuration confirmed per order.

Services

Compression
as a Service.

Edge AI is the inevitable direction — but four walls stand between a cloud model and a working on-device product.

Compression as a Service dashboard showing upload, optimize, validate, and download steps, plus model size comparison and job status
Compression as a Service · upload, optimize, validate, download
Memory WallA 7B model in FP16 needs ~14GB — far beyond edge device limits.
Compute WallEdge NPU compute is limited; native models can't run in real time.
Power WallHeavy inference power draw — battery life and thermals both suffer.
Accuracy WallNaive quantization collapses accuracy into an unusable result.
What You Get · Four Deliverables

Compressed Model Package

Quantized weights + operator library + inference config — runs out of the box.

Benchmark Report

Accuracy / speed / memory, before vs. after, measured on your real target hardware.
 

Kernative Studio Access

Full toolchain — quantize → distill → validate → package, visualized end to end.

Custom Engineering Support

Source-level customization for your model, platform, or business metric.
Five-Step Delivery Flow
1
Model Assessment
Baseline & platform check
2
Compression Plan
Bit-width & pruning strategy
3
Compress & Validate
Accuracy regression & benchmarks
4
Deployment Tuning
On-device performance tuning
5
Delivery & Support
Package, report & iteration
4–8×Compression Ratio
<2GB7B Model Memory (from 14GB)
2–4×Inference Speedup
<3%Accuracy Loss (MMLU)
~70%Power Reduction

Figures represent typical targets and measured ranges; actual results depend on customer model, platform, and business requirements.

Typical delivery: 2–4 weeks for a compression engagement · 24/7 technical support through production · on-demand model and platform expansion.

Silicon Partners

Built on the chips
you already ship.

Certified quantization and runtime optimization across two major NPU platforms — with more silicon partners in active qualification.

Qualcomm
Qualcomm

Hexagon NPU kernel-level optimization across the automotive and mobile Snapdragon families.

SA8295PAutomotive Cockpit SoC
SA8775PAutomotive ADAS SoC
SA8397Automotive 5G SoC
SA8650PAutomotive SoC
Snapdragon 8 Gen 3Mobile Flagship SoC
AMD
AMD

Runtime and quantization tuning for AMD's client and edge AI accelerators.

Ryzen AIClient / Laptop NPU
Versal AI EdgeAdaptive Edge SoC

Silicon and product names are trademarks of their respective owners.

Use Cases

Target industries &
applications.

Featured Case Study

Smart cockpit, offline — a 7B model running in the car, full pipeline on-device.

Smart Cockpit Automotive-Grade Module Fully Offline Data Privacy
Need

A Tier-1 automotive supplier needed in-cabin voice and multimodal interaction that works with zero connectivity, low latency, and strict data privacy.

Solution

A 7B conversational model compressed to INT4 and deployed on an automotive-grade module, customized on Kernative AIBox capability — full local inference, integrated with the in-cabin OS.

Impact

Cold start <1s · first-token <80msFully functional offline, with all cabin data staying on the vehicle.

On-device deployment walkthrough · model running locally on target silicon

Products used: Kernative Compress · Kernative Inference · Kernative AIBox (automotive-grade custom configuration).

Automotive

In-vehicle voice assistant and cockpit display
In-vehicle voice assistants, driver monitoring, smart cockpit, ADAS perception, fleet management.
87ms latency on SA8295P · 100% offline for safety-critical systems

Industrial

Factory-floor robotic arm with AI vision inspection
Edge AI for the factory floor, predictive maintenance, robotics brain, quality inspection, worker safety.
Zero cloud dependency for IP-sensitive manufacturing environments

Consumer Electronics

AI features across laptop, phone, watch and glasses
AI PCs, smartphone AI features, smart home hubs, wearables, AR/VR contextual AI.
Battery-efficient inference · always-on AI without cloud costs

AI Model Companies

Model deployment dashboard across edge targets
Ship "edge versions" of cloud models, silicon-specific optimization, reduce time-to-market.
Go from cloud-only to edge-ready in weeks, not months
Customer Pain Points We Solve
We have a great model, but it won't fit on our NPU.

We quantize it to INT8/INT4 with <1% loss — it fits, and it runs fast.

Our NPU utilization is only 35%.

We optimize kernels, fuse layers, tune memory layout — get to 85%+.

We can't send user data to the cloud.

100% on-device inference. Zero data leaves the device. Ever.

We need this in production in 8 weeks.

<7-day quantization + integration = production-ready in 6 weeks.

Partnership

How we work together.

1
Discovery
Week 1–2
2
POC
Week 3–6
3
Integration
Week 7–10
4
Production
Week 11–14
5
Support
Ongoing
NRE — One-Time Engineering
Custom quantization + NPU optimization for a specific model and chip.
Typical timeline: 6–12 weeks
License — Annual
Ongoing access to the quantization toolchain, updates, and support.
Typical timeline: Annual renewal
Our Recommendation

Start with NRE — focused project delivery with clear scope and timeline.

Network node icon
Why Kernative AI

Six reasons teams
choose us.

QUALCOMM NATIVE

Speaks Hexagon fluently

Built ground-up for Snapdragon NPU — not retrofitted from a generic toolchain.

SILICON-SPECIFIC

Hardware-aware, not generic

Tuning for each NPU architecture, not one-size-fits-all compression.

END-TO-END

One team, one contract

From model to NPU runtime — one throat to choke, start to finish.

CROSS-CONTINENTAL

16-hour coverage

California + Asia-Pacific — always-on delivery.

FLEXIBLE

No lock-in

NRE or License. Scale the engagement as your program grows.

PRODUCTION-READY

Real silicon, real products

Not research demos — deployment experience on production hardware.

Edge AI chip icon

Bring your model to the edge.

Tell us your model and target chip — we'll scope a POC.

Email Kernative AI
Contact
General Inquiriesinfo@kernative.com
Partnerships & BDinfo@kernative.com
EngineeringAsia-Pacific team · 16-hour timezone coverage
HeadquartersCalifornia, USA
Engagement at a Glance
Business ModelNRE (project) or License (annual)
Typical POC3–6 weeks
Production Ready11–14 weeks from kickoff
Silicon CoverageQualcomm · AMD · more in qualification