yasp's agents profile your model on real silicon, generate optimized kernels for the target hardware, and validate every result. Faster models. More silicon options. Less time between research and production.
One workflow. The assistant orchestrates. The engine generates.
The builder ships. The memory remembers.
yasp's agents don't just write kernels. They compile, run them on the actual target
hardware, measure performance and correctness, and feed the results back into the next iteration. Every kernel you receive is verified, never a suggestion.
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Drop-in replacement for your existing PyTorch model. Works with TorchServe, Triton,
FastAPl, and any PyTorch-based serving framework. Full PyTorch features available,
including training. Same code, faster execution, identical outputs..
Self-contained, no PyTorch in production. Compatible with any CUDA version, OS, or
runtime version. Custom kernels generated for any op TensorRT doesn't support
natively. Currently optimized for NVIDIA edge: Jetson Orin and Drive AGX.
Coverage
PyTorch in. Hardware-optimized output. Vendor and architecture coverage grows every quarter, driven by what customers ship.
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.
{ IBM Granite 4.0 • NVIDIA H200 }
Kernel fusion, FP16 lowering, vectorized loads, and
wavefront-level reductions on Azure's AMD V710.
Measured against torch.compile on the actual silicon.
{ Speedups that compound }
Verified on real silicon across hybrid LLMs,
transformer blocks, vision models, and novel
architectures. Numerical correctness held within
strict tolerances on every run.
The status quo locks teams into single-vendor ecosystems. yasp compiles your model for
the hardware you choose, validates on real silicon, and gets out of the way.
The vendor's ecosystem dictates which chips you target. Switching costs months of re-engineering.
Optimizing for non-default hardware requires compiler engineers most teams can't hire.
Traditional compilers produce code deterministically. They don't adapt —
performance is left on the table.
Models that work in the cloud need painful re-engineering for edge devices. Edge stays theoretical.
yasp optimizes for the chip you specify. Change the target? Recompile. No new
toolkit.
Not lowest common denominator - performance tuned specifically for the target you chose.
yasp's compiler learns from every compilation. The search space is too large to hardcode — so yasp uses Al to navigate it.
Compile for cloud and edge from the same model. Pre-built images pushed to
devices.
For company and business FAQs, see the homepage.
Every kernel is validated against the PyTorch reference within strict tolerances. Validation results are included with every compilation and surfaced in the agent's output. If a candidate kernel doesn't meet the threshold, the system iterates until it does, or falls back to the reference implementation.
yasp traces through standard PyTorch and third-party libraries. Novel layers like Mamba are supported. Operations that can't be optimized fall back to the reference implementation, so correctness is never compromised by ambition.
Cache hits via KernelDB are near-instant. First-time compilations depend on complexity: the agents may explore for up to 30 minutes on a fresh model as they generate and validate candidate kernels. Subsequent runs on the same hardware return in seconds.
Yes. yasp supports lower-precision execution (FP16, BF16, INT8) when the target hardware benefits and your accuracy thresholds allow it. The main focus is kernel generation and hardware-specific optimization, where the largest gains usually come from.
yasp operates on the mathematical graph of your nn.Module. Model weights stay in your environment and are never transmitted. KernelDB stores verified kernels and performance profiles, not your architecture or training data.
No. Whether you ship optimized PyTorch via yasp.codegen or a hardware-native binary via yasp.native, the output is self-contained. No SDK, no runtime, no persistent layer. Deploy anywhere the target hardware is available.
