A render farm is not a collection of GPUs. It is a machine that converts time into frames. Its output is measured in reciprocal duration.
Q382597 (render_farm):
Computer system, e.g. a computer cluster, for rendering computer-generated imagery (CGI).
Q7798498 (throughput):
In business, the rate of movement of inputs and outputs through a production process.
ISQ dimension: T⁻¹
The Wikidata graph binds these concepts. A render farm is a production process. Its output is throughput. Throughput is fundamentally inverse time.
| T | Total frames rendered per hour | frames·hr⁻¹ ≡ T⁻¹ |
| N | Number of nodes in cluster | count |
| F | Frames per second per GPU (base throughput) | frames·s⁻¹ |
| S | Scene complexity factor | dimensionless |
| 3600 | Seconds per hour (normalization constant) | s·hr⁻¹ |
Dimensional analysis confirms the form:
[T⁻¹] = [count] × ([frames·s⁻¹] ÷ [1]) × [s·hr⁻¹]
[T⁻¹] = [frames·hr⁻¹] ✓
Antonio etched this in code and steel: 140 ft-lbs, star pattern, three passes. The hub law is not metaphor. It is the torque specification that keeps the wheel true. The same law governs the cluster.
A render farm scales linearly with node count because each node contributes independently to the aggregate throughput. There is no magic in parallelization — only arithmetic.
SIMPLE SCALING LAW:
T(nodes=k) = k × T(nodes=1)
Double the nodes, double the throughput. Halve the scene complexity, halve the time per frame.
The theory is useless without computation. The throughput calculator implements this equation. Input your cluster spec, your GPU baseline, your scene load — it returns the raw T⁻¹.
Grounded in Wikidata. Machine-readable JSON. Human-executable HTML.