HPC Systems Launches AI Infrastructure Assessment Service
HPC Systems has launched a new "AI Infrastructure Assessment" service to support the implementation of generative AI and LLMs in companies and research institutions. The service proposes optimal hardware configurations tailored to model size and concurrent users, from PoC to full-scale production.
📋 Article Processing Timeline
- 📰 Published: April 11, 2026 at 00:30
- 🔍 Collected: April 11, 2026 at 00:21
- 🤖 AI Analyzed: April 20, 2026 at 05:53 (221h 31m after Collected)
HPC Systems Inc. (Headquarters: Minato-ku, Tokyo; Representative Director: Teppei Ono; hereinafter, HPC Systems) has launched a new service, "AI Infrastructure Assessment," to support the implementation of generative AI and LLMs in companies and research institutions.
This service organizes configuration designs and verification perspectives, from PoC environments to full-scale implementation, based on conditions such as model size, number of concurrent users, context length, and response performance, and presents them as a report.
Depending on customer requirements, the service organizes configurations including GPU memory capacity, inference methods, parallel configurations, storage, and networks.
In recent years, while the adoption of generative AI and LLMs in companies and research institutions has progressed, decisions regarding hardware configurations have become more complex, considering factors such as model size, number of concurrent users, context length, response performance, on-premise requirements, and future scalability. In particular, configurations that were effective during the PoC phase may not be suitable for the performance requirements and scale of full-scale operation, increasing the importance of requirements organization and configuration design in the initial stages of implementation.
To date, our company has provided proposals and construction support for high-performance computing infrastructure, primarily through the provision of HPC servers, as well as GPU-equipped servers and workstation products in the field of computational science. The new service, launched this time, leverages this expertise to present configuration proposals and verification perspectives, from PoC to full-scale implementation, in a report format, based on customer usage assumptions and operational conditions, thereby supporting decision-making for generative AI and LLM implementation.
This service considers usage conditions such as model size, number of concurrent users, and context length, and examines configurations including GPU memory capacity, inference methods, and parallel configurations. Furthermore, in addition to GPU server configurations, it provides comprehensive configuration organization, including storage, networks, and inference software stacks, with a view to practical operation.
Moreover, this service views implementation considerations in three stages: "Evaluation Introduction," "Production Selection," and "Post-Implementation Optimization." This allows for separate organization of configurations suitable for initial verification and those necessary for production operation in terms of performance, scalability, and operational requirements. By clarifying the necessary verification perspectives and challenges at each stage, a final configuration proposal report is presented.
Service Overview
Based on customer interviews, this service provides a report including the following:
Recommended 3 configuration options and their prerequisites
Expected performance range (including response performance and concurrent usage assumptions)
PoC verification items
Additional challenges during transition to production
Estimated quotation
In addition, the considerations are organized in the following three stages:
Evaluation Introduction: Configuration organization assuming multiple model trials and initial verification
Production Selection: Organization of production configurations based on the number of concurrent users, response performance, and operational conditions
Post-Implementation Optimization: Organization of challenges with a view to scalability, operational load, and additional tuning
This enables customers to move beyond comparing GPU specifications and product features, and to proceed with validating the suitability of PoC environments, organizing challenges during production implementation, and considering future expansion policies, all in a manner tailored to their specific usage scenarios.
Target Audience
This service primarily targets the following types of companies and research institutions:
Companies considering generative AI utilization, including internal document search and RAG
Companies considering LLM operation in on-premise or closed environments
Companies that have built a PoC environment and want to organize their transition policy to a production configuration
Companies that want to compare and examine requirements for selecting GPU-equipped servers and workstations
Companies and research institutions that want to leverage their knowledge of high-performance computing infrastructure, cultivated for computational science and R&D purposes, to develop infrastructure for generative AI.
This service organizes configuration designs and verification perspectives, from PoC environments to full-scale implementation, based on conditions such as model size, number of concurrent users, context length, and response performance, and presents them as a report.
Depending on customer requirements, the service organizes configurations including GPU memory capacity, inference methods, parallel configurations, storage, and networks.
In recent years, while the adoption of generative AI and LLMs in companies and research institutions has progressed, decisions regarding hardware configurations have become more complex, considering factors such as model size, number of concurrent users, context length, response performance, on-premise requirements, and future scalability. In particular, configurations that were effective during the PoC phase may not be suitable for the performance requirements and scale of full-scale operation, increasing the importance of requirements organization and configuration design in the initial stages of implementation.
To date, our company has provided proposals and construction support for high-performance computing infrastructure, primarily through the provision of HPC servers, as well as GPU-equipped servers and workstation products in the field of computational science. The new service, launched this time, leverages this expertise to present configuration proposals and verification perspectives, from PoC to full-scale implementation, in a report format, based on customer usage assumptions and operational conditions, thereby supporting decision-making for generative AI and LLM implementation.
This service considers usage conditions such as model size, number of concurrent users, and context length, and examines configurations including GPU memory capacity, inference methods, and parallel configurations. Furthermore, in addition to GPU server configurations, it provides comprehensive configuration organization, including storage, networks, and inference software stacks, with a view to practical operation.
Moreover, this service views implementation considerations in three stages: "Evaluation Introduction," "Production Selection," and "Post-Implementation Optimization." This allows for separate organization of configurations suitable for initial verification and those necessary for production operation in terms of performance, scalability, and operational requirements. By clarifying the necessary verification perspectives and challenges at each stage, a final configuration proposal report is presented.
Service Overview
Based on customer interviews, this service provides a report including the following:
Recommended 3 configuration options and their prerequisites
Expected performance range (including response performance and concurrent usage assumptions)
PoC verification items
Additional challenges during transition to production
Estimated quotation
In addition, the considerations are organized in the following three stages:
Evaluation Introduction: Configuration organization assuming multiple model trials and initial verification
Production Selection: Organization of production configurations based on the number of concurrent users, response performance, and operational conditions
Post-Implementation Optimization: Organization of challenges with a view to scalability, operational load, and additional tuning
This enables customers to move beyond comparing GPU specifications and product features, and to proceed with validating the suitability of PoC environments, organizing challenges during production implementation, and considering future expansion policies, all in a manner tailored to their specific usage scenarios.
Target Audience
This service primarily targets the following types of companies and research institutions:
Companies considering generative AI utilization, including internal document search and RAG
Companies considering LLM operation in on-premise or closed environments
Companies that have built a PoC environment and want to organize their transition policy to a production configuration
Companies that want to compare and examine requirements for selecting GPU-equipped servers and workstations
Companies and research institutions that want to leverage their knowledge of high-performance computing infrastructure, cultivated for computational science and R&D purposes, to develop infrastructure for generative AI.