# How Aleph Alpha uses Weights & Biases

**62,000 models**: “the team has used W&B to train 62,000 models over the span of 271,000 hours”

As published on [wandb.ai](https://wandb.ai/site/customers/aleph-alpha/), October 2023. Captured by usedby on 2026-10-08.

- Company: [Aleph Alpha](https://www.usedby.ai/companies/aleph-alpha.md)
- Tool: [Weights & Biases](https://www.usedby.ai/tools/weights-and-biases.md)
- Industry: [Artificial Intelligence](https://www.usedby.ai/companies/industry/artificial-intelligence.md)

## What the story says

Aleph Alpha uses Weights & Biases to track, compare and manage the large number of experiments involved in training its large language models. The team also uses it to monitor system utilization for hardware optimization and to share results in a common workspace.

Summary written by usedby from the source page, in English. The figures are those of Weights & Biases and Aleph Alpha, not ours.

> To date, the team has used W&B to train 62,000 models over the span of 271,000 hours, with their longest training run being 960 hours.

- **271,000 hours**: “To date, the team has used W&B to train 62,000 models over the span of 271,000 hours, with their longest training run being 960 hours.”

> W&B gives us a concise look at all projects. We can compare runs, aggregate them all in one place and intuitively decide what works well and what to try next.
>
> Samuel Weinbach, VP of Technology

Source: [wandb.ai](https://wandb.ai/site/customers/aleph-alpha/), captured 2026-10-08.

## What usedby checked

We compared the story with its live page on 2026-10-08.

- Checked: the figure 62,000 models is printed word for word on the page, near the name of Aleph Alpha.
- Checked: the passage quoted above is copied word for word from the page, near the name of Aleph Alpha.
- Checked: the publication date is read from the page’s own metadata, never guessed.
- Checked: Aleph Alpha uses Weights & Biases. Confirmed line. Latest check across sources: 2026-10-08.
- Not checked: the result itself. We quote it; we did not measure it.

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Source: https://www.usedby.ai/case-studies/aleph-alpha-weights-and-biases · How we check: https://www.usedby.ai/methodology