There is no single RAM minimum for a local AI translator: memory use depends on the model, runtime, precision, input size, and other apps running. As a cautious planning target for a general-purpose computer, consider 16 GB of system RAM—but that is not an official or tested minimum. A useful reference point is Meta’s NLLB-200 distilled 600M model: Hugging Face estimates 2.13 GB of model memory at float16/bfloat16, but that is a model-sizing estimate, not the total RAM a particular translation app will need.
What RAM capacity should you plan for?
If you want one practical target for a new or upgraded general-purpose computer, 16 GB of system RAM is a cautious recommendation for running a modest local translator while leaving capacity for the operating system and other applications. It is a planning target, not a guarantee that a particular translator will run well, or an official requirement from a model vendor.
A smaller or optimized translator may work on a computer with less RAM. Before buying an upgrade, check the exact app’s requirements and the model and runtime it uses. No comparable peak total-system-RAM measurements are established for the named apps across different hardware and workloads, so a single app-independent minimum cannot be given.
Use NLLB-200 as a reference, not a universal minimum
Meta’s NLLB-200 distilled 600M is a multilingual encoder-decoder translation model. Hugging Face’s 2023 model-sizer-bot discussion estimates its model memory at different numerical precisions:
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| Precision | Estimated model memory |
|---|---|
| float32 | 4.25 GB |
| float16/bfloat16 | 2.13 GB |
| int8 | 1.06 GB |
| int4 | 544.49 MB |
These are model/VRAM sizing estimates, not measurements of total system RAM for a complete translation application. The utility says inference may need up to 20% additional memory; that is its caveat, not a universal overhead measurement. Its minimum recommended VRAM calculation assumes model placement with Accelerate/device_map and is based on the largest layer. Do not read the table as a promise that a computer with the same amount of system RAM will run NLLB successfully.
The model’s configuration lists 12 encoder layers, 12 decoder layers, maximum position embeddings of 1024, and a generation maximum length of 200. Those settings help identify the model configuration, but do not establish a peak-RAM formula for every input, batch, runtime, or app. Hugging Face’s memory-estimate discussion and the model configuration provide the underlying details.
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Do not confuse model download size with RAM
Meta’s NLLB-200 distilled 600M repository is about 2.48 GB on disk. That is storage space for the model files, not the RAM the translator needs while running. RAM is working memory; model-file size and working memory are related but are not interchangeable measures. Meta’s repository files show the download footprint.
Smaller offline systems exist, but a small download does not prove that an application has equally small total runtime memory use. Argos Translate installs language-pair model packages. A 2021 TranslateLocally paper describes a tiny English-German Bergamot model with a 15 MB download size; that figure is model download size, not a measured RAM requirement. The paper also discusses latency and variation in consumer hardware. Argos Translate documentation and the TranslateLocally paper describe these options.
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What changes a local translator’s memory use?
- Model and language coverage: Different translation systems and language pairs use different models. Confirm that the option supports your languages directly; Argos can pivot through an intermediate language when no direct pair is installed, and its documentation warns that this may reduce quality.
- Precision and runtime: The NLLB estimates vary substantially between float32 and lower-precision formats. Whether an app supports a given precision, and how it places work on CPU or GPU, depends on its implementation.
- Input and batch size: Longer inputs and larger batches can affect working memory. The available configuration details do not establish a universal peak-memory figure for a document or batch.
- Other software: The operating system, browser, and other running applications also use system RAM, so model-memory figures alone are not a whole-computer capacity recommendation.
- Quality and domain: A model’s broad benchmark result does not guarantee the same quality for every language pair or subject area. In its 2022 paper, the NLLB Team evaluated more than 40,000 translation directions and reported a 44% BLEU improvement relative to the prior state of the art in its stated comparison; that result is scoped to the paper’s benchmark, not every translation task. Read the NLLB paper for its evaluation context.
Check before upgrading your computer
- Identify the exact translator and model. Find its current requirements and confirm the language pair you need. “Offline translator” does not identify one model or one memory footprint.
- Separate storage from working memory. Make sure you have disk space for the model download, but do not use that file size as the RAM requirement.
- Confirm hardware support. Check whether the app uses CPU, GPU, or both, and whether it supports the model precision you intend to run.
- Leave headroom. Account for the operating system and your normal applications rather than allocating all installed RAM to the model.
- Test the actual workload when possible. Try your language pair and typical input size on the target computer before purchasing RAM. If an upgrade is needed, verify the computer’s memory type, available slots, and supported maximum capacity; model estimates do not identify a compatible module.
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