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Guides / Scaling guide

Scaling guide

When does a private AI setup need a cluster?

Start with a capacity or concurrency constraint you can measure.

By Web Asset House · data reviewed 2026-10-22

A cluster is a group of machines coordinated to perform work. It is useful only when the intended workload and runtime can use it. More networked memory does not automatically become a single fast pool, and adding nodes does not guarantee faster token generation.

Three reasons to consider more than one machine

  1. You have a model that cannot fit a practical single-machine configuration, and a supported distributed runtime.
  2. You need separate models or independent requests running concurrently.
  3. You need operational separation or redundancy that your application can actually use.

Measure the bottleneck before adding hardware

Determine whether the problem is model capacity, prompt-processing latency, generation speed, or concurrent demand. These lead to different designs. A network can become a bottleneck when model execution requires repeated transfers between machines. Count power, cooling, cabling, management time, and software support in the total cost.

What this estimator does not model

The calculator is a single-machine planning tool. Multi-GPU entries record aggregate physical VRAM for reference, but a fit cannot be confirmed without a placement and interconnect model. Distributed network inference is outside its speed formula. Do not multiply its output by the number of GPUs or nodes.

Continue with the cluster guide

For Web Asset House’s separate multi-node resource, visit Personal AI Cluster. Confirm the current design, hardware, and runtime support before relying on a proposed build.

Primary sources

Questions, answered.

Will two computers produce twice the tokens per second?

Not necessarily. The runtime, model partitioning, network, and workload determine scaling. This calculator does not estimate distributed inference.

When is a single larger-memory computer simpler?

When it meets the model capacity and concurrency requirements, a single machine avoids distributed placement and network coordination requirements.