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6 Oct 2026Form Number
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Table of Contents
Introduction
This sizing tool helps you determine the hardware and infrastructure required to deploy large language models for a wide range of AI workloads. Whether you're building a chatbot, Retrieval-Augmented Generation (RAG) application, AI agent, code assistant, or another generative AI solution, this tool helps you estimate the hardware resources needed to meet your performance and scalability requirements.
Version 2.0. Click the Full Change History link to see what's new.
How to use the tool
Scroll down to use the tool. To get started:
- Select the Use Case
- Select the desired GPU Type
- Click the Calculate GPU Requirements button
The tool automatically populates recommended preset values for the model and workload parameters, including concurrency, input and output token lengths, precision, and other deployment settings, which you can adjust as needed. Based on these inputs, it analyzes your workload and recommends an optimal infrastructure configuration. For models or workloads not included in the presets, you can create custom use cases and models to generate tailored sizing estimates.
The sizing results include recommended GPU count, VRAM utilization, server platform, CPU and system memory requirements, storage, network adapters, and switching infrastructure. It also estimates key inference performance metrics, including Time to First Token (TTFT), inter-token latency, end-to-end request latency, throughput, and maximum supported concurrent users, enabling you to evaluate different deployment scenarios before provisioning hardware.
Use this tool to compare infrastructure options, optimize resource utilization, and make informed decisions when designing scalable and cost-effective AI deployments on Lenovo infrastructure. All estimates are based on analytical models and benchmark data and should be used as guidance for capacity planning rather than guaranteed production performance.
Use the Subscribe to Updates link above to get notified when the tool gets updated, and use the Feedback link to send us comments and suggestions.
Configure and Buy
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Full Change History
Changes in Version 2.0, published October 6, 2026:
- CPU Sizing: New tab for sizing CPU and memory requirements
- Memory breakdown: Clearer pie chart showing how GPU memory is used, also included in the PDF report
- Hybrid platform recommendations: The tool now suggests a matching Lenovo Hybrid AI platform for your workload
- More GPUs and models: Added NVIDIA Blackwell B300 and RTX PRO Blackwell GPUs, plus newer popular models
- Power: Estimated power usage now shown in the results
- Updated interface: Redesigned PDF report, improved dark mode and general layout tidy up
- Better accuracy: Improved latency, throughput and memory estimates, including support for MoE models and longer context lengths
Course Detail
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