# WeatherNext Memory Requirements and VRAM Usage: Full vs. Mini Models Compared

> Discover WeatherNext memory requirements for full vs Mini models. Learn VRAM needs for H100 and P100 GPUs, plus system RAM usage to optimize your setup.

- Repository: [Google DeepMind/weathernext](https://github.com/google-deepmind/weathernext)
- Tags: performance
- Published: 2026-08-12

---

**The full-resolution WeatherNext models require ≥80 GB VRAM (H100 GPU) and ≥25 GB system RAM, while the Mini models run on ≤16 GB VRAM (P100 GPU) and ≤12 GB RAM.**

Memory and computational requirements vary dramatically between the two WeatherNext model families. Understanding these differences helps you select the right hardware configuration for your forecasting workload without encountering out-of-memory errors during inference.

## Full-Resolution WeatherNext Models: Memory Footprint

The non-Mini WeatherNext models operate at full spatial resolution with complete pressure level coverage. According to the repository's [`README.md`](https://github.com/google-deepmind/weathernext/blob/main/README.md), these models demand high-end accelerators due to their large weight tensors and activation memory.

### VRAM Requirements

The full models need **at least 80 GB of GPU VRAM**, targeting NVIDIA H100 GPUs as the reference hardware. Older GPUs—including 40 GB and 80 GB A100 variants—may struggle with the largest checkpoints. The repository explicitly states: *"The non-Mini models require H100 for sufficient VRAM"*【/cache/repos/github.com/google-deepmind/weathernext/main/README.md†L107-L108】.

### System RAM Requirements

Host memory demands are equally substantial. For inference with comparable full-resolution models like GraphCast-small, you'll need **at least 25 GB of system RAM** when running on TPUv4 or A100 accelerators【/cache/repos/github.com/google-deepmind/weathernext/main/README.md†L223-L233】. This accommodates the input grids (ERA5/HRES data) and intermediate activations during autoregressive rollout.

## WeatherNext Mini Models: Lightweight Alternative

The **Mini** models provide a deliberately constrained architecture for resource-constrained environments. These variants reduce spatial resolution to 1° and limit pressure levels, trimming both weight count and activation memory.

### VRAM Requirements

Mini models fit comfortably within **≤16 GB of GPU VRAM**, making them compatible with NVIDIA P100 GPUs and newer laptop-class accelerators. The repository confirms: *"The Mini models should manage inference on a P100"*【/cache/repos/github.com/google-deepmind/weathernext/main/README.md†L107-L108】.

### System RAM Requirements

Host memory needs drop proportionally to **≤12 GB of system RAM**. This enables deployment on standard workstations without specialized server hardware.

## Loading and Running: Code Implementation

Both model tiers share identical APIs through `weathernext.load_model()`. The checkpoint name determines which variant loads and which memory constraints apply.

### Loading a Full-Resolution Model

```python
import weathernext as wn

# Requires H100/A100 GPU (≥80 GB VRAM)

full_model = wn.load_model(
    checkpoint="WeatherNextCyclones_2025",  # Full-resolution

    device="gpu",
)

full_forecast = full_model.autoregressive_rollout(
    initial_state=wn.utils.load_initial_state(...),
    num_steps=4,
)

```

### Loading a Mini Model

```python

# Runs on P100 or similar (≤16 GB VRAM)

mini_model = wn.load_model(
    checkpoint="WeatherNextCyclones_Mini_2024",  # 1° resolution

    device="gpu",
)

# Identical API, different memory footprint

mini_forecast = mini_model.autoregressive_rollout(
    initial_state=wn.utils.load_initial_state(...),
    num_steps=4,
)

```

The `wn.load_model` implementation in [`weathernext/weathernext1_cyclones/model.py`](https://github.com/google-deepmind/weathernext/blob/main/weathernext/weathernext1_cyclones/model.py) handles weight-loading logic automatically. The same `autoregressive_rollout` function from [`weathernext/utils/autoregressive.py`](https://github.com/google-deepmind/weathernext/blob/main/weathernext/utils/autoregressive.py) serves both model tiers—switching from full to Mini requires only a checkpoint name change【/cache/repos/github.com/google-deepmind/weathernext/main/weathernext/weathernext1_cyclones/model.py】【/cache/repos/github.com/google-deepmind/weathernext/main/weathernext/utils/autoregressive.py】.

### Monitoring VRAM Usage

Verify memory constraints with JAX device utilities:

```python
import jax
print(jax.devices()[0].memory_stats())  # Bytes allocated, peak usage

```

This helps confirm Mini models stay within the ~16 GB target during extended rollouts.

## Hardware Recommendations by Use Case

| Scenario | Recommended Model | Minimum GPU | Minimum RAM |
|----------|-------------------|-------------|-------------|
| Production forecasting, research | Full (non-Mini) | NVIDIA H100 | 25 GB |
| Prototyping, education, edge deployment | Mini | NVIDIA P100 | 12 GB |
| Colab demos, quick experiments | Mini | T4/V100 (free tier) | 12 GB |

The `docs/weathernext2/wn2_demo.ipynb` notebook demonstrates both model tiers and explicitly notes accelerator requirements for each【/cache/repos/github.com/google-deepmind/weathernext/main/docs/weathernext2/wn2_demo.ipynb】.

## Summary

- **Full models**: ≥80 GB VRAM (H100), ≥25 GB RAM, full 0.25° resolution
- **Mini models**: ≤16 GB VRAM (P100), ≤12 GB RAM, reduced 1° resolution
- **API parity**: Same `wn.load_model()` and `autoregressive_rollout()` functions for both tiers
- **Single-line switching**: Change only the checkpoint name to toggle between model families

## Frequently Asked Questions

### Can I run full WeatherNext models on multiple smaller GPUs?

The repository documentation focuses on single-accelerator deployment. The `weathernext.load_model()` implementation does not expose multi-GPU sharding in the public API. For distributed inference, you would need to implement custom JAX parallelism around the core model weights in [`weathernext/weathernext1_cyclones/model.py`](https://github.com/google-deepmind/weathernext/blob/main/weathernext/weathernext1_cyclones/model.py).

### Why do Mini models use 1° resolution instead of 0.25°?

The resolution reduction in Mini checkpoints directly decreases both weight tensor dimensions and activation grid sizes. This architectural constraint—documented in [`docs/weathernext1_cyclones/README.md`](https://github.com/google-deepmind/weathernext/blob/main/docs/weathernext1_cyclones/README.md)—enables the 5× VRAM reduction while preserving core forecasting capabilities for demonstration and lightweight applications.

### How do I confirm my GPU has enough memory before loading?

Check available device memory before model initialization using `jax.devices()[0].memory_stats()` or `nvidia-smi`. The Mini models target ≤16 GB peak allocation, but actual usage varies with batch size and rollout length. Start with short `num_steps` values and monitor memory growth during `autoregressive_rollout()` execution.

### Are Mini model predictions significantly less accurate?

Mini models trade spatial precision for accessibility. The 1° resolution limits fine-scale feature representation compared to full 0.25° models, particularly for convective processes and localized extreme events. For synoptic-scale patterns and medium-range guidance, Mini outputs remain qualitatively useful—as demonstrated in the official Colab notebooks.