Will it fit and perform?
VRAM, quantization, concurrency, and single-server topologies.
Explore the calculator ↗TECHNICAL PROGRESS AT YOUR TEAM’S PACE
Make sense of AI.
Balance your hardware.
Build with confidence.
The most direct route to bringing generative AI into your projects. Clear resources and transparent calculations to move from concept to rack, knowing exactly what to expect at every stage.
For systems engineers, architects, DevOps, and IT managers.
01 / Sound judgment for confident decisions02 / Built for everyday IT work03 / Figures with their sources and assumptions in view04 / Open assumptions behind every figure
01 / THE QUESTIONS THAT MATTER
Before choosing a server,
understand what you need.
GPU capacity is only part of the picture. Your decision also depends on the workload, operations, budget, and data governance.
What will it cost?
Hardware, energy, operations, and cloud costs over a 3-to-5-year horizon.
Read: on-premises AI vs. cloud ↗Where does the data live?
Control, security, and governance principles for on-premises and hybrid environments.
Explore content (Spanish) ↗How is it maintained?
Inference engines, virtualization, orchestration, backups, and continuity.
Visit the campus (Spanish) ↗02 / AVAILABLE TOOL
Less guesswork.
More configuration.
Match LLM requirements to hardware. Adjust resources, check memory headroom, and download a report for your scenario.
Configure my solution ↗Indicative estimates; they do not replace load testing.
LLM CALCULATORBeta
VRAMCapacity and headroom
- Model and precision
- Context and users
- Tokens per user
- Energy and PDF report
Lenovo + generic hardware View methodology ↗
03 / TECHNICAL RESOURCES
Evidence before promises.
How much VRAM does an LLM need? A sizing guide
Learn to calculate the VRAM an LLM needs from its parameters, precision, and KV cache. Formulas, examples, and a calculator for your own use case.
Read guide ↗LLM quantization: FP16 vs INT8 vs INT4
What LLM quantization means, how it affects VRAM and performance, and how to choose FP16, INT8, or INT4 for your use case.
Read guide ↗What is an LLM’s KV cache, and why does it use so much memory?
What an LLM’s KV cache is, how it grows with context and concurrency, and how to estimate its memory needs when sizing your server.
Read guide ↗FROM THEORY TO YOUR ENVIRONMENT
Planning an AI deployment?
Start with the workload, constraints, and objectives.