The 32GB Ceiling: Why Open Source Weather Models Aren't Truly Open

Last month, I spent nearly a week compiling Fortran code to simulate Typhoon Kalmaegi. The goal was straightforward: I wanted to run a local weather forecast, on my own hardware, tuned for the area I live in here in Thailand.

I used the Weather Research and Forecasting (WRF) model. It worked. The outputs were accurate and detailed. But the cost was high. Each simulation required days of continuous compute, dense and unforgiving namelist configurations, and a dependency chain that felt less like software installation and more like archaeology.

Naturally, I started looking for something better. Or at least faster.

The headlines were promising. “AI predicts weather 10,000x faster than numerical models.” “Global forecasts in seconds.” The implication was clear: weather modeling was about to become accessible. Anyone with a GPU, we were told, could run a global forecast from their desk.

Tools like Google's GraphCast, Huawei's Pangu-Weather, and ECMWF's AIFS promise to replace heavy physics calculations with rapid neural inference. Theoretically, this makes weather intelligence more open to anybody, suggesting that anyone with a GPU can now run a global weather station from their desk.

I set out to build exactly that: a local, AI-driven weather model to replace the aging Fortran stack. Instead, I ran into a new constraint. One that was harder than the last.

I call it the 32GB Ceiling.

The Inference Gap

In traditional software, “Open Source” usually implies accessibility. If you can install it, you can run it. The primary cost is time.

In modern AI workflows, meteorology included, the code may be free, but the runtime is not. The bottleneck has shifted from time to space. Specifically, memory. Massive tensor operations now dominate the cost, and without sufficient VRAM, execution simply stops.

To understand where the limits actually lie, I audited several widely cited “local” AI weather models, focusing on whether they could realistically run on high-end consumer hardware. Namely the common 24GB ceiling of flagship GPUs, and even the newer 32GB tier.

And this is how it works out.

Tier 1: Institutional Compute

Some models are effectively unreachable outside data centers.

GraphCast and Pangu-Weather fall into this category. While their code is publicly available, their standard inference pipelines assume data-center-class accelerators. Running GraphCast at 0.25° resolution typically requires around 60GB of VRAM. That places it squarely in A100 territory.

These projects are “open” in the legal sense, but operationally inaccessible. You can study them, but you cannot meaningfully run them. Access is granted to institutions, not individuals.

Tier 2: The Marginal Zone

This is where things become uncomfortable.

Models like AIFS and FourCastNet sit just below the institutional tier, but still above what consumer hardware can comfortably support. AIFS, for example, defaults to roughly 38GB of VRAM for ensemble inference.

On a 24GB GPU, this forces aggressive compromises: chunked inference, mixed precision, careful memory juggling. It runs, but only just. You are constantly at the edge of failure.

Even with newer 32GB consumer cards, the situation barely improves. The margin is thinner, the optimizations less extreme, but the fundamental mismatch remains. You are paying a premium for hardware that still cannot execute the model as designed.

Tier 3: Constrained by Design

Only the most recent, experimental work appears to acknowledge the hardware gap directly.

Models like WeatherMesh-3 and KAI-a are explicitly designed to fit within 16–24GB memory envelopes. They recognize the constraints of real-world users and attempt to work within them.

The trade-off, of course, is maturity. These systems lack the validation, track record, and institutional confidence of their larger counterparts. They are promising, but not yet proven.

Physics vs. Tensors

This investigation clarified something fundamental about the state of engineering in 2025.

We haven't truly "democratized" weather prediction yet; we've just changed the currency required to buy it.

With WRF, the cost was patience. Given enough time, a modest CPU could eventually solve the equations. With modern AI models, the cost is memory bandwidth. If you do not have the VRAM, the model does not degrade gracefully, it simply does not run.

For a student or researcher, the barrier hasn't been removed; it has just changed shape.

For now, I am sticking with WRF. It is slow, painful, and grounded in 20-year-old Fortran. But it has one distinct advantage over the state-of-the-art AI models:

It actually fits on my laptop, and it runs. (AMD Ryzen 5 4600H, 14GB RAM, runs as LXC container inside Proxmox.)