Large Language Models with NVIDIA and JedAI

JedAI provides a collaborative development environment for Data Science teams to experiment, train, fine-tune, and deploy LLMs or GenAI workloads across the enterprise and in the Cloud, on NVIDIA® powered GPUs.

Artificial intelligence-based natural language processing (NLP) has graduated from being a far-fetched concept to a highly sought-after technology. Large language models (LLMs) fuel these NLP-based applications, translating speech-to-text, training chatbots, aiding search engines, and more. Though LLMs hold significant potential, they require massive computing power to operate efficiently. In this blog, we will dive into how JedAI delivers on the infrastructure needs for NLP and large language models.

JedAI’s IaaS architecture, which allows virtual machines to run on top of physical servers, makes it the ideal platform to manage LLM infrastructure. NLP tools are notorious for their computational payloads, which means that running one instance requires multiple CPU cores, memory, and high-speed network connectivity. By leveraging OpenStack, CTOs, and developers can easily deploy a scalable infrastructure that gives seamless access to comparable computing power.

As the name suggests, LLMs essentially use extensive language datasets, which require high-performance file storage. With JedAI’s Swift storage service, managed compute resources can access data stored on highly available distributed storage. This capability results in lower latency and faster data transfer, effectively addressing the bottleneck problems that high-volume data storage requirements create.

JedAI’s ability to orchestrate resources to meet changing workloads makes it an ideal technology for LLM infrastructure. The training, validation, and inference stages of an LLM’s life cycle require different computing resources. For example, the training stage requires higher CPU and GPU capacities than the other stages. With JedAI, infrastructure managers can scale compute instances to meet the varying needs of an LLM, dynamically allocating resources to where they are needed most.

JedAI’s security features make it an ideal infrastructure choice for LLMs. NLP workloads often require access to sensitive data such as healthcare records and financial information. With JedAI, infrastructure managers can securely manage access to sensitive systems and data, implementing controls such as role-based access controls (RBAC), virtual private networks (VPNs), and more.

Large language models are quickly becoming a key tool for organizations looking to enhance their natural language processing capabilities. JedAI’s scalable, secure, and flexible infrastructure makes it ideal for managing the heavy lifting behind these models. With JedAI, CTOs, and developers can deploy and manage large constructed language models, which speed up application deployment and rollout, resulting in more efficient NLP-based applications.


General Enquiry

Large Language Models with NVIDIA and JedAI

JedAI provides a collaborative development environment for Data Science teams to experiment, train, fine-tune, and deploy LLMs or GenAI workloads across the enterprise and in the Cloud, on NVIDIA® powered GPUs.

Artificial intelligence-based natural language processing (NLP) has graduated from being a far-fetched concept to a highly sought-after technology. Large language models (LLMs) fuel these NLP-based applications, translating speech-to-text, training chatbots, aiding search engines, and more. Though LLMs hold significant potential, they require massive computing power to operate efficiently. In this blog, we will dive into how JedAI delivers on the infrastructure needs for NLP and large language models.

JedAI’s IaaS architecture, which allows virtual machines to run on top of physical servers, makes it the ideal platform to manage LLM infrastructure. NLP tools are notorious for their computational payloads, which means that running one instance requires multiple CPU cores, memory, and high-speed network connectivity. By leveraging OpenStack, CTOs, and developers can easily deploy a scalable infrastructure that gives seamless access to comparable computing power.

As the name suggests, LLMs essentially use extensive language datasets, which require high-performance file storage. With JedAI’s Swift storage service, managed compute resources can access data stored on highly available distributed storage. This capability results in lower latency and faster data transfer, effectively addressing the bottleneck problems that high-volume data storage requirements create.

JedAI’s ability to orchestrate resources to meet changing workloads makes it an ideal technology for LLM infrastructure. The training, validation, and inference stages of an LLM’s life cycle require different computing resources. For example, the training stage requires higher CPU and GPU capacities than the other stages. With JedAI, infrastructure managers can scale compute instances to meet the varying needs of an LLM, dynamically allocating resources to where they are needed most.

JedAI’s security features make it an ideal infrastructure choice for LLMs. NLP workloads often require access to sensitive data such as healthcare records and financial information. With JedAI, infrastructure managers can securely manage access to sensitive systems and data, implementing controls such as role-based access controls (RBAC), virtual private networks (VPNs), and more.

Large language models are quickly becoming a key tool for organizations looking to enhance their natural language processing capabilities. JedAI’s scalable, secure, and flexible infrastructure makes it ideal for managing the heavy lifting behind these models. With JedAI, CTOs, and developers can deploy and manage large constructed language models, which speed up application deployment and rollout, resulting in more efficient NLP-based applications.


General Enquiry