Optimize your model for any hardware.

yasp takes your model and generates optimized, hardware-specific code.
No rewrites. No vendor lock-in.

Pick a model and a target, then hit Run. yasp compiles and validates on real silicon.

MODEL

TARGET HARDWARE

MODE

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

yasp compile

Run

$ yasp compile \
   --model llama-3-1-405b \
   --target nvidia-b200 \
   --mode inference
$

Pick a product, a model, and a target, then hit Run. yasp does the specialist work and validates on real silicon.

Product
Model
Target hardware

Deploy to edge takes a research model to a production binary on the Nvidia Jetson platform — in minutes, not weeks.

yasp edge deploy
Deploy →
Run
$ yasp edge deploy \
--model llama-3-2-3b \
--target jetson-agx-thor
$
Get Started
Get Started
Speak with an Engineer
Speak with an Engineer
Validated on real hardware from AMD, NVIDIA, and Amazon

We Support

Memberships & Programs

--light-rainbow-12

Change the flag, 
recompile in minutes.

Your CUDA code only runs on NVIDIA — Your kernels are written for one vendor's toolchain. Deploy on AMD or Qualcomm? Start over. New compiler, new optimization, new debugging cycle.

The status quo

What yasp changes

Portability costs you performance

Non-default hardware gets generic code. The compiler engineers who could fix it barely exist outside three companies. Most teams stay on NVIDIA.

Compilers settle for good enough

Fixed optimization passes. Every new architecture, every model revision — another cycle from scratch. 2-10x performance left on the table, indefinitely.

Locked to one vendor's toolchain

Your model runs on NVIDIA because the compiler only speaks CUDA. Switch to AMD or Qualcomm? Months of re-engineering.

Change the target. Recompile.

Same PyTorch model, different hardware flag. No framework rewrite. No new runtime. MiniGPT on AMD V710: faster than NVIDIA A10, 67% lower cost.

Tuned kernels for the hardware you chose.

yasp generates hardware-specific code — vectorized memory access, precision-aware computation, matched to the target's execution model. Not lowest common denominator.

An agentic compiler that adapts.

AI agents navigate the optimization search space. Every kernel verified on real silicon. Every optimization stored and compounding.

The status quo

Portability costs you performance

Non-default hardware gets generic code. The compiler engineers who could fix it barely exist outside three companies. Most teams stay on NVIDIA.

Compilers settle for good enough

Compilers settle for good enough

Locked to one vendor's toolchain

Your model runs on NVIDIA because the compiler only speaks CUDA. Switch to AMD or Qualcomm? Months of re-engineering.

Change the target. Recompile.

Same PyTorch model, different hardware flag. No framework rewrite. No new runtime. MiniGPT on AMD V710: faster than NVIDIA A10, 67% lower cost.

Tuned kernels for the hardware you chose.

yasp generates hardware-specific code — vectorized memory access, precision-aware computation, matched to the target's execution model. Not lowest common denominator.

An agentic compiler that adapts.

AI agents navigate the optimization search space. Every kernel verified on real silicon. Every optimization stored and compounding.

See benchmarks
See benchmarks
Free trial
Free trial
How yasp works

From trained model to optimized deployment. Nothing in between.

Most AI infrastructure adds a layer between you and your hardware. yasp removes it. Bring a trained model and a hardware target. yasp compiles it to the target hardware — no dependencies, no vendor platform in the middle. The output runs natively on your target. yasp transforms and exits.

Bring your model

Any PyTorch model — training or inference. Custom architectures, foundation models, research code. No ONNX export. No workflow changes.

yasp optimizes for your target

yasp profiles your model against the target. Cached optimization? Instant. New target? The agentic compiler generates, verifies, and stores one. Every optimization feeds back into the shared library.

Deploy. yasp is gone.

Cloud or edge. yasp delivers a self-contained binary that runs natively on your target. No persistent layers, no forced runtimes, no new dependencies. Recompile, don't rewrite.

Bring your model

Any PyTorch model — training or inference. Custom architectures, foundation models, research code. No ONNX export. No workflow changes.

yasp optimizes for your target

yasp profiles your model against the target. Cached optimization? Instant. New target? The agentic compiler generates, verifies, and stores one. Every optimization feeds back into the shared library.

Deploy. yasp is gone.

Cloud or edge. yasp delivers a self-contained binary that runs natively on your target. No persistent layers, no forced runtimes, no new dependencies. Recompile, don't rewrite.

Get Started
Get Started
Speak with an Engineer
Speak with an Engineer
Products

Start with Gaia.

Ship with Kronos.

Gaia finds the performance. Kronos ships it. Same compiler underneath, and every number on both sides came off real hardware.

Kronos

Path to Nvidia production

PyTorch model in, self-contained binary out. Compiles for Jetson Orin, Drive AGX, H100, H200, and other Nvidia silicon. Custom modules compile natively. Weeks of deployment work collapse into a single guided pipeline.

Learn more
Learn more
Gaia

Max throughput, multi-vendor

Inference and kernel optimization across Nvidia, AMD, and AWS Trainium. Agents iterate on inference graphs and kernels at machine speed, finding performance humans miss. CUDA, HIP, Triton output.

Learn more
Learn more
Explore all products
Explore all products
Proven on real silicon

No Claims. Just Facts.

AI infrastructure is full of performance claims that are hard to compare and harder to trust. yasp validates generated code and optimization results on real hardware. We earn credibility by making verification easy.

Performance

9x

IBM Granite 4.0 inference on NVIDIA H200 vs torch.compile. Full methodology published. Our most validated benchmark.

Performance

~$900k/mo

Compute savings for autonomous driving customer. 30% reduction on $2-3M/mo spend. Months → weeks dev cycles.

Hardware

67%

Cost reduction — MiniGPT on AMD V710 vs NVIDIA A10

Performance

6.25x

Granite 4.0 Mamba layer — novel architecture optimized automatically

Model

6.8x

Inference speedup on AMD RX 9070 (47.2ms → 6.9ms)

Performance

9x

IBM Granite 4.0 inference on NVIDIA H200 vs torch.compile. Full methodology published. Our most validated benchmark.

Performance

~$900k/mo

Compute savings for autonomous driving customer. 30% reduction on $2-3M/mo spend. Months → weeks dev cycles.

Hardware

67%

Cost reduction — MiniGPT on AMD V710 vs NVIDIA A10

Performance

6.25x

Granite 4.0 Mamba layer — novel architecture optimized automatically

Model

6.8x

Inference speedup on AMD RX 9070 (47.2ms → 6.9ms)

Load more benchmarks

Platformless AI Infrastructure

The model is yours.

The hardware is your choice.

No new stack. No new dependency. No new vendor to serve. Start with docs and benchmarks, or talk to the team about your deployment environment.

Get Started
Get Started
Speak with an Engineer
Speak with an Engineer

Real results on real hardware

Teams using yasp to reduce deployment friction and improve performance on real hardware. 
Not theory. Not projections. Measured outcomes.

Tab 1
Tab 2
Physical AI

How an autonomous trucking company reduced perception deployment costs by 30% with yasp

Torc Robotics is an autonomous vehicle technology company developing Level 4 self-driving systems for long-haul commercial trucks.

2-4x

Speedup

Months to Days

Time to market

30%

Cost reduction across perception workloads

Read the full story
Read the full story
Cloud Training & Inference

Yasp and AMD: Unlocking High-Performance AI Inference on AMD Radeon™ PRO V710 GPUs

Microsoft Azure is one of the world's largest cloud platforms, spanning NVIDIA and AMD GPU compute across global regions.

Up to 67%

Lower cost per inference vs NVIDIA A10

$2.00 vs $3.20

GPU hourly rate on Azure

Zero

Lines of model code changed

Read the full story
Read the full story
Cloud Training & Inference

How yasp found the optimization in IBM Granite 4.0 that no engineer had time to find

IBM Granite is IBM's family of open, enterprise-grade AI models. Granite 4.0 introduced a Mamba hybrid architecture for high-efficiency inference.

Up to 3x

End-to-end model speedup

6.25x

Faster Mamba layer inference

Read the full story
Read the full story
For developers and enterprise

An Enterprise scale solution built for the Builders.

Whether you're a developer shipping to silicon or an enterprise team optimizing across hardware platforms - yasp compiles, optimizes, and deploys in minutes.

{ For developers }

Start building with yasp

The chip you want becomes the chip you use. Full docs, quickstart guide, and API reference. Get optimized code on real hardware in minutes. No new stack to learn. No new dependency to manage.

Get Started
Get Started
Read the Docs
Read the Docs

{ For enterprise }

Hardware decisions become engineering decisions again

The chip you want becomes the chip you use. Full docs, quickstart guide, and API reference. Get optimized code on real hardware in minutes. No new stack to learn. No new dependency to manage.

Speak with an Engineer
Speak with an Engineer
See case studies
See case studies
Get Started
Get Started
Book a demo
Book a demo
Industry use cases

Your model runs wherever 
you send it.

yasp serves teams deploying AI across cloud GPUs, edge SoCs, autonomous platforms, and custom robotics. One compilation pipeline. Any hardware target.

Autonomous Systems

Compile vision models directly to NVIDIA DRIVE platforms (Orin, Thor). Automated multi-SoC partitioning. Days, not months.

Learn more
Learn more
Robotics

Deploy across varied hardware targets without rebuilding. Cloud training to on-device inference — same compilation pipeline.

Learn more
Learn more
Cloud Training & Inference

Reduce cloud compute costs, unlock AMD alongside NVIDIA. 67% cost reduction in real benchmarks.

Learn more
Learn more
Consumer-Sale Inference

Serve millions of queries faster and cheaper.

Learn more
Learn more
Model Labs & AI Platform

Ship your model to every chip, without per-target perf teams.

Learn more
Learn more
Edge AI

Deploy AI to constrained edge devices.

Learn more
Learn more

FAQ

Straight answers to the questions a skeptical engineer would actually ask.

No items found.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

yasp is not a platform — and that’s the point. It compiles to the metal and leaves. No persistent layer, no runtime, no new ecosystem. A new stack is just a new dependency. yasp removes dependencies.

All topics
Home

Every benchmark validated on physical hardware. Methodology documented. If we claim a number, you can check it.

All topics
Home

No. Bring a PyTorch model and a target. Your existing code, your existing training loop. yasp compiles and gets out of the way.

All topics
Home

Both. Same compilation pipeline. Compile for cloud today, retarget to edge tomorrow.

All topics
Home

Teams deploying models to real hardware across diverse targets. Especially Physical AI — where hardware diversity is the default.

All topics
Home