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Ease and Efficiency

Train and deploy your own ML models while reducing compute and
maintaining model accuracy.

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Cost Effective Cost Effective

More efficient cycles means reduced costs.

Faster Faster

Experience up to 8x acceleration of your model inference.

Safer Safer

Model code and accuracy stay intact.

Deploy with the fastest and the most cost efficient platform

Elevate performance while curbing costs with CentML. Enhance GPU efficiency, slash latency, and boost throughput effortlessly. Deploy with CentML and make computing cost-effective and powerful.

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Case Studies

Examples of what we have done for our clients

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A leading AI company in the pricing of financial derivatives


In monthly savings


Depending on the model and hardware choices

A top financial AI company partnered with CentML, gaining a 65x performance boost using consumer-grade GPUs and CentML's technology. This reduced costs significantly, outperforming Intel's CPUs by 10x with a 15x cost advantage. CentML's optimizations resulted in a 1.5x inference speedup, optimizing CPU resources and delivering cost savings for the client's financial operations.

AI company specializing in foundational models


In monthly savings


Depending on the model and deployment platform choices

An AI firm, with roots in AI research, teamed up with CentML to boost inference speed by 65% and increase throughput nearly 7x for large LLMs. Traditional methods caused delays, but CentML's Hidet compiler provided a solution with GPU kernel optimization. Clients surpassed SLAs, enhancing their AI models' efficiency.

Generative-AI company specializing in conversational knowledge analysis


In monthly savings


Inference acceleration

A conversational AI company partnered with CentML, achieving a 2x model inference speedup and 50% throughput improvement with NVIDIA V100 GPUs. CentML's graph optimization resulted in over 2x speedup on small batch sizes and a 1.7x improvement on larger ones. They overcame GPU challenges with Microsoft DeepSpeed. This partnership enhances their API-as-a-Service, providing a superior customer experience.

Meet CServe

Revolutionizing model serving for the LLM era with unparalleled speed,
cost-efficiency and simplicity.

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Our Products

Our Open Source

Our Open Source

Explore the dynamic duo of optimization with Hidet, our open-source compiler, and DeepView, the open-source profiler. Together, they empower AI engineers to unlock performance, gain invaluable insights, and transform the way they develop their models.

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Hidet is an open-source deep learning compiler, written in Python. It supports end-to-end compilation of DNN models from PyTorch and ONNX to efficient cuda kernels. A series of graph-level and operator-level optimizations are applied to optimize the performance.

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CentML DeepView provides an integrated experience which allows ML practitioners to visually identify model bottlenecks, perform rapid iterative profiling, understand energy consumption and environmental impacts of training jobs, and predict deployment time and cost to cloud hardware.

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Technologies you are developing will revolutionize Deep Learning optimization and capacity availability.

Misha Bilenko
VP of AI @ Microsoft Azure

Software is King. Architecture without a good compiler is like a race car without a good driver. I'm excited to be advising a company that is helping push the state-of-the-art in ML as well as help with reductions in carbon emissions.

David Patterson
Distinguished Engineer @ Google

With the breakneck pace of generative AI, we're always looking for an edge to stay ahead of the competition. One main focus is optimizing our API-as-a-Service for speed and efficiency, to provide the best experience for our customers. CentML assisted Wordcab with a highly personalized approach to optimizing our inference servers. They were patient, attentive, and transparent, and worked with us tirelessly to achieve inference speedups of 1.5x to 2x.

Aleks Smechov
CEO & Co-founder @ Wordcab

The most innovative Deep Learning is usually coded as a sequence of calls to large general purpose libraries. Until recently we had little or no sophisticated compiler technology that could transform and optimize such code, so the world depended on library maintainers to manually tune for each important DL paradigm and use case. Recently compilers have begun to arrive. Some are too low level and others are too specialized to high level paradigms. CentML's compiler tech is just right -- powerful, flexible, and impactful for training and inference optimization.

Garth Gibson
Former CEO & President @ Vector Institute

I'm excited to be advising a company that is helping push the state-of-the-art in ML as well as help with reductions in carbon emissions.

David Patterson
Distinguished Engineer @ Google

Amazing team, conducting cutting edge work that will revolutionize the way we are training and deploying large-scale deep learning systems.

Ruslan Salakhutdinov
CMU Professor

With CentML's expertise, and seamless integration of their ML monitoring tools, we have been able to optimize our research workflows to achieve greater efficiency, thereby reducing compute costs and accelerating the pace of our research.

Graham Taylor
Vector Institute for Artificial Intelligence

The proliferation of generative AI is creating a new base of developers, researchers, and scientists seeking to use accelerated computing for a host of capabilities. CentML’s work to optimize AI and ML models on GPUs in the most efficient way possible is helping to create a faster, easier experience for these individuals.

Vinod Grover
Senior Distinguished Engineer and Director of CUDA and Compiler Software at NVIDIA

CentML’s compiler technology is proven to deliver impressive training and inference optimizations. We anticipate these solutions will bring significant benefits for our ML model development efforts, helping us best serve our customers.

Tamara Steffens
TR Ventures Managing Director

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