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Model Memory Fit · ASR

Whisper medium: memory fit on every Jetson module.

Method v1.0 · dataset 2026-09-07 · verified 2026-09-07 · last updated September 2026

0.77 B parameters · 24 layers · 448-token max context. Hugging Face: openai/whisper-medium.

Model facts

FactValueNote
Parameters0.769 B
Layers24
Hidden size1024
Attention heads16
KV heads16
Head dimension64
Max context448 tokens
dtypefloat32
Vocabulary51,865

OpenAI · hf:openai/whisper-medium · config.json · verified 2026-09-07

“"d_model": 1024, "encoder_layers": 24, "decoder_layers": 24, "encoder_attention_heads": 16, "decoder_attention_heads": 16”

Parameters source

  • OpenAI · github:openai/whisper/README.md · verified 2026-09-07 · openai/whisper GitHub README.md · class A
    “| medium | 769 M | `medium.en` | `medium` | ~5 GB | ~2x |”

Published quantised artefacts

A file the publisher or a community mirror actually ships, used as the class-A weight figure when the requested quant matches. Any quant without a row here falls back to parameters × bytes-per-parameter (class D for block quants).

No published quantised artefact for this model; weights are estimated as parameters × bytes-per-parameter for every quantisation.

Jetson tokens/s measurements

Published or archived throughput numbers, not modelled. Class C (external measured benchmark).

No Jetson-measured tokens/s reported for this model yet.

Every module × every quantisation

Verdict and total memory at 4096-token context, 1 sequence, llama.cpp, headless. Q4 cells link to the static breakdown page; every other cell links to the live engine at that quantisation.

Notes

Encoder-decoder architecture; fields per-stack (encoder==decoder here). Reference VRAM requirement '~5 GB' from OpenAI README is a generic estimate, not Jetson-measured.

Method and data: Model Memory Fit methodology. Full registry: model-memory.json.

Change the context, concurrency or runtime.

The live engine covers any context length, sequence count, KV precision and runtime, with a permanent link.

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