> ## Documentation Index
> Fetch the complete documentation index at: https://docs.monostate.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Rate Limits

> Understanding rate limits and quotas

# Rate Limits

AITraining rate limits apply when using cloud resources.

## Local Training

Local training has no rate limits - you're only limited by your hardware.

## Hugging Face Hub

When pushing to or pulling from the Hub:

| Operation         | Rate Limit |
| ----------------- | ---------- |
| Model downloads   | Fair use   |
| Dataset downloads | Fair use   |
| Model uploads     | Fair use   |

### Handling Rate Limits

If you hit rate limits:

```python theme={null}
import time
from huggingface_hub import HfApi

def download_with_retry(model_id, max_retries=3):
    api = HfApi()
    for attempt in range(max_retries):
        try:
            return api.model_info(model_id)
        except Exception as e:
            if "rate limit" in str(e).lower():
                wait = 60 * (attempt + 1)
                print(f"Rate limited, waiting {wait}s...")
                time.sleep(wait)
            else:
                raise
```

## W\&B Logging

Weights & Biases has logging limits based on your plan:

| Plan       | Logged Hours/Month |
| ---------- | ------------------ |
| Free       | 200                |
| Teams      | Unlimited          |
| Enterprise | Unlimited          |

### Reducing Log Volume

```python theme={null}
params = LLMTrainingParams(
    model="google/gemma-3-270m",
    data_path="./data.jsonl",
    project_name="my-model",
    log="wandb",
    logging_steps=50,  # Log less frequently
)
```

## GPU Cloud Services

If using cloud GPUs (not applicable to local training):

### Hugging Face Spaces

* Limited by your Spaces quota
* Persistent storage limits apply

### Other Clouds

Check your cloud provider's quotas for:

* GPU hours
* Storage
* Network bandwidth

## Best Practices

1. **Cache models locally** - Don't re-download
2. **Log efficiently** - Don't log every step
3. **Use checkpoints** - Resume instead of restart
4. **Batch operations** - Reduce API calls

## Next Steps

<CardGroup cols={2}>
  <Card title="Error Handling" href="/api/error-handling">
    Handle errors gracefully
  </Card>

  <Card title="Python SDK" href="/api/python-sdk">
    Full API reference
  </Card>
</CardGroup>