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LLM VRAM
requirements.

Start with the model, not the GPU. Compare parameter count, native context, architecture, and a consistent 4-bit memory estimate before tuning the workload.

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Reading the table

Q4 estimates use one 8K sequence plus runtime workspace. They are planning estimates—not measured throughput—and the exact runtime, quantization method, adapters, and graph capture can change real usage.

01 / Weights

Parameters set the floor.

Weight memory is driven by parameter count and precision. Moving from BF16 to a 4-bit format can cut the raw weight footprint by roughly four times, but metadata and higher-precision layers mean the final reduction is not perfectly linear.

Learn how to calculate LLM VRAM →
02 / Workload

Context changes the answer.

KV cache grows with sequence length and concurrent requests. A model that fits comfortably for an 8K single-user test can need materially more memory at 32K context or production concurrency.

Understand context and KV cache →
Model chosen?

Turn the model into a fit plan.

Set context and concurrency in the calculator, then compare the recommended memory envelope with the hardware directory.

Calculate VRAMCompare GPU profiles