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A published MXFP4 example is conditional on its implementation; it is not a BF16 estimate or a guarantee for every GPU and context.
For gpt-oss-20b VRAM planning, first verify the artifact format and implementation. An official MXFP4 example does not describe the same footprint in BF16 or establish support on every GPU. Record the actual loading path, context and reasoning configuration.
A useful local test identifies one supported combination rather than making a universal recommendation from the model name.
Separate total and active parameters
OpenAI describes gpt-oss-20b with approximately 21 billion total and 3.6 billion active parameters. Memory planning cannot count only the active part as though the other stored weights were absent.
Preserve the full identifier and revision. Do not confuse the original artifact with another conversion or a safeguard variant solely because it shares a family name. Licensing and intended external use require their own review.
Read the MXFP4 conditions
OpenAI’s Transformers guide connects its memory guidance to MXFP4 and a supported implementation. A different representation can have a substantially different footprint. The downloaded file size does not describe every runtime allocation.
| Record | Why it is needed |
|---|---|
| GPU and driver | Hardware and software support |
| Library versions | Reproducible execution path |
| Artifact revision | Exact weights under test |
| Actual format | Distinguishes intended and used paths |
| Request limits | Defines serving memory needs |
Check what the runtime actually reports, if available. Merely seeing MXFP4 in the model card does not establish that an arbitrary environment uses the expected path.
Start with a control request
Use a known-answer question and bounded output. Record reasoning mode and expected output representation. If service channels or unexpected formatting appear, inspect template and parser handling before concluding that the model is faulty.
Use synthetic or anonymised test data. Moving to a real workflow requires separate quality criteria. A successful load and a useful completed answer are distinct acceptance stages.
Expand memory one factor at a time
Increase input while holding output fixed, then test longer generation separately. Add concurrency afterwards. If memory fails after startup, identify the failing stage rather than assuming the weights alone explain it.
Repeat after restarting the process. Record whether files were downloaded again and whether preparation changed. Also preserve how the GPU is released back to its owner.
Technical feasibility is not a promise of an approved sales partnership, continuous demand or a return on the hardware.
Continue with gpt-oss-120b VRAM: Total MoE Weights and Active Parameters and CUDA Out of Memory While Loading a Model: A Check Order.