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The model has far more total weights than active parameters per token; supported execution and full service memory must be checked together.
For gpt-oss-120b, budget the complete mixture-of-experts artifact rather than only active parameters per token. Official placement examples depend on a supported format and hardware configuration. Adding the memory of several smaller GPUs does not automatically reproduce the same execution arrangement.
The assessment should preserve artifact preparation, loaded memory and active request use as separate observations.
Keep both parameter counts
OpenAI lists approximately 117 billion total and 5.1 billion active parameters for gpt-oss-120b. The second figure does not replace the first when planning stored weights. Different experts participate according to the architecture, but the complete artifact is not a 5.1B model.
Do not compare it with a dense model using only whichever parameter figure produces the most attractive ranking. Record both concepts and their purpose.
Verify format and loading conditions
OpenAI’s Transformers guide discusses MXFP4 and specific software conditions. Do not transfer that footprint to another weight representation without verification. Preserve revision, driver, libraries and actual loading method.
If the runtime converts or expands components, record that behaviour. Measure memory after startup separately from file size and active request memory. Keep storage space and weight-cache locations visible because they affect preparation and recovery.
Distinguish execution arrangements
| Arrangement | Additional question |
|---|---|
| One suitable GPU | Full runtime and request footprint |
| Several GPUs in one host | Supported model partitioning |
| GPUs at different sites | Network and stack suitability |
| Another weight format | Compatibility and quality revalidation |
The table is a set of questions, not a promise that every arrangement is available. For multiple devices, record allocation individually rather than relying only on the combined total.
Test a bounded useful workload
Begin with a short session and known correct result. Preserve reasoning mode, output representation and generation limit. Increase input separately from concurrency. Count useful completion, not merely the absence of a startup error.
Measure a normal restart and time back to readiness. Large-file preparation is not token-generation speed, but it matters operationally. Record downloaded components, storage requirements and any manual intervention needed to restore service.
A smaller model may be a better candidate if it solves the same task within practical limits. A defined, tested workload is the basis for assessment; model size alone does not establish utilisation or revenue.
Continue with gpt-oss-20b VRAM: Read the Conditions Behind the Claim and How Much VRAM Does a 70B Model Need?.