Making GenAI
accessible for everyone
We provide the framework for enterprises to easily build and train
high-performing customized GenAI models and serve them at low cost.
We provide the framework for enterprises to easily build and train
high-performing customized GenAI models and serve them at low cost.
Solution
Solution
Lite LLMOps
Lite LLMOps
Lite LLMOps is a solution that automates and optimizes the entire process of selecting, training, evaluating, and deploying language models for enterprise clients. It offers several tailored solutions for different customer needs at various stages of the LLMOps cycle: the Model Builder solution for customers needing automatic model development, the Model Compressor, Model Accelerator, and Query Router solutions for those looking to reduce serving costs, and the Model Evolver solution for clients aiming to maintain stable performance
Lite LLMOps is a solution that automates and optimizes the entire process of selecting, training, evaluating, and deploying language models for enterprise clients. It offers several tailored solutions for different customer needs at various stages of the LLMOps cycle: the Model Builder solution for customers needing automatic model development, the Model Compressor, Model Accelerator, and Query Router solutions for those looking to reduce serving costs, and the Model Evolver solution for clients aiming to maintain stable performance
Solution 2
Solution 2
Lite Space
Lite Space
Lite Space is a research-friendly, on-premise, and cloud-based AI research and development platform designed to help AI researchers utilize limited GPUs more efficiently and effectively. It provides features necessary for AI research, including team-based GPU quota scheduling, team-based job scheduling, usage reports, and Research Mentoring Agents, thus enhancing research productivity and maximizing GPU usage.
Lite Space is a research-friendly, on-premise, and cloud-based AI research and development platform designed to help AI researchers utilize limited GPUs more efficiently and effectively. It provides features necessary for AI research, including team-based GPU quota scheduling, team-based job scheduling, usage reports, and Research Mentoring Agents, thus enhancing research productivity and maximizing GPU usage.
Solution Usage Scenario 01
Serving Cost Reduction
Serving Cost Reduction
Cost reduction technology from the infrastructure layer to the model layer,
enabling up to 96.5% cost savings
Cost reduction technology from the infrastructure layer to the model layer, enabling up to 96.5% cost savings
(1) Tested on 1000 unseen queries from various domains, comparing scenarios where the client exclusively uses specific commercial APIs (e.g. GPT-4)
(1) Tested on 1000 unseen queries from various domains, comparing scenarios where the client exclusively uses specific commercial APIs (e.g. GPT-4)
(2) When A100 or H100 GPUs are required to load the target model, our method optimizes the model to enable the use of more cost-effective GPUs, such as A10 or L40.
(2) When A100 or H100 GPUs are required to load the target model, our method optimizes the model to enable the use of more cost-effective GPUs, such as A10 or L40.
(3) We offer H100 and A100 GPUs at prices over 30% lower than AWS.
(3) We offer H100 and A100 GPUs at prices over 30% lower than AWS.
Solution Usage Scenario 02
Training Cost Reduction
Training Cost Reduction
Cost reduction technology from the infrastructure layer to the model layer, enabling up to 99% cost savings when utilizing all layers
Cost reduction technology from the infrastructure layer to the model layer, enabling up to 99% cost savings when utilizing all layers
(1) We observe a 75% faster evaluation speed compared to normal evaluation scenarios by compressing evaluation benchmarks and estimating scores using a performance predictor.
(1) We observe a 75% faster evaluation speed compared to normal evaluation scenarios by compressing evaluation benchmarks and estimating scores using a performance predictor.
(2) We achieve 90% faster training speed than typical training scenarios through parameter-efficient fine-tuning and hyper parameter optimization
(2) We achieve 90% faster training speed than typical training scenarios through parameter-efficient fine-tuning and hyper parameter optimization
(3) We reduce search costs by 50% compared to normal search scenarios by retrieving optimal models from our model hub with over 10,000 state-of-the-art models.
(3) We reduce search costs by 50% compared to normal search scenarios by retrieving optimal models from our model hub with over 10,000 state-of-the-art models.
(4) We offer H100 and A100 GPUs at prices that are more than 30% lower than AWS.
(4) We offer H100 and A100 GPUs at prices that are more than 30% lower than AWS.
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Naver D2 Startup Campus, Seoul, South Korea 🇰🇷
200 Rivserside Blvd #18G, New York, USA 🇺🇸
© DeepAuto.ai All rights reserved. Privacy Policy.