Deep learning was enabled by hardware, and its progress is now limited by software
Deep learning is hardware-enabled, yet constrained by its limitations. Models and datasets are growing faster than hardware advances, making optimization feel like a never-ending puzzle. Even when AI developers find the optimal performance configuration, the cost and power consumption are often unsustainable.
Our mission at Lemurian Labs is to serve you, the AI developer, by reimagining computing from your perspective. We’ve built a truly unified compiler and software stack that outperforms CUDA, freeing you from hardware-specific limitations and brittle APIs. With our compiler, achieving unmatched speed, efficiency, and portability is as simple as running your models on any hardware, anywhere.
We have a visceral understanding of this problem because we have walked miles in the shoes of the AI developer. We know that the problem is multidimensional. We know that one or two improvements will not be enough. We have to reimagine computing from the lens of the AI developer. That means a true truly unified platform that shields you from the hardware complexity and creates a step function improvement in performance, efficiency and developer productivity
Our Tech makes it so you never need to pore through reams of hardware documents again
01
Our Full Stack Solution
Our software stack, built from first principles, ingests PyTorch and runs it efficiently across all hardware, allowing seamless transitions from training to inference. As opposed to traditional stacks that require custom-built APIs and multiple tool integrations, our single stack is all you need to target and optimize your models—delivering a fully unified runtime experience.
Our compiler offers broad frontend support to seamlessly integrate with Triton, PyTorch, and JAX as the frontend, letting you focus on building your models without concern for hardware compatibility. Performance is our top priority, and our compiler is designed to maintain peak speed across all platforms.
Ingest Pytorch models and execute on any hardware
We created a compiler for developers so they never have to redo the insipid work of optimizing the same model for a different hardware.
02
Hardware-Aware Performant Portability
Our compiler is hardware agnostic, supporting all types of compute, ie GPUs, CPU, NPU, etc for hardware-aware compilation articulating hardware at the top of the stack to make more intelligent task mapping decisions. We decompose the model in to task graphs and decompose the target hardware to basic compute blocks based on their individual ISAs. Mapping task graphs to ISAs is tedious and slow when done manually, but a perfect use case for automation through our software.
Getting a model to meet performance and cost targets requires a specialized skillset and knowledge, from a deep understanding of the model architecture and the target hardware memory structure and ISA. If a different hardware platform is targeted, that entire workflow starts all over again, beginning with the optimization engineer learning another architecture and ISA. We built our compiler to ensure developers never need to worry about low-level hardware documentation again. Whether you are deploying on MI300, other advanced hardware, or any compute architecture, our compiler delivers unmatched speed and performance.
As a result of our innovative approach, we also deliver model portability that can take in any previously optimized model and retarget to new/different hardware with no human intervention, delivering the same or better performance. This level of portability is unprecedented—no other tool or stack of tools today can achieve this.
Port optimized models to any hardware and maintain performance
We solved a 250 year old math problem
03
New Data Type for More Efficient and Reliable AI
We solved a 250 year old math problem to create a breakthrough in computing efficiency. Our innovative logarithmic data type not only has better representation than floating-point, it enables an astounding increase in efficiency. Additionally, the smaller, more efficient data representation can be used to significantly increase memory efficiency when used as a compression technique. This smaller representation retains accuracy and precision, while making memory constrained hardware architectures more efficient.
This boost allows us the potential to break free from legacy approaches to parallel computing. By revolutionizing the way you compute, we empower you to achieve more with less.
8-bit number line spectra for FP, PAL, INT, LNS.
