Total Parameters vs Active: What Your GPU Cares About

Close-up of a graphics card with glowing memory chips, illustrating GPU memory used by large language models

 

This month's big model releases each came with two numbers. Reflection Beam: 501 billion parameters, 23 billion active. Mistral Large 4: 1 trillion parameters, around 50 billion active. These are the companies' own figures from their October announcements. If you run models locally, you need to know which number your hardware cares about.

The active number sets your speed. In a mixture-of-experts (MoE) model, only a slice of the network fires for each token. Inference cost behaves closer to a 23B model than a 501B one. That is why these giant models can still serve cheaply.

But the total number sets your memory. Every parameter has to sit in video memory (VRAM) or unified memory before a single token is generated, firing or not. Beam's 23B active sounds workstation-friendly until you do the math: all 501B parameters at 8-bit precision need roughly 500 GB of memory. At 4-bit quantization, about half that. Either way, it is a multi-GPU server, not a desktop.

So read model specs in this order: total parameters for the memory bill, active parameters for the speed estimate. Quantization shrinks the memory bill, but it can never shrink it below what the full weight set demands.

Want to see what fits on your machine? The VRAM calculator on llmhardware.io sizes models against real GPUs, including quantized builds.

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