GPU Memory Exhaustion During Model Fitting on NVIDIA T4

[GeethaJoseph]

Hi team,

I'm running into a GPU memory issue while fitting an MMM model and would appreciate some guidance on whether this appears to be a hardware limitation or a potential configuration issue.

Model Configuration

• Time periods: 66 weeks
• Geographies (DMAs): 183
• Media channels: 36
• Controls: 10 non-media variables + 8 controls

Compute Environment

Databricks cluster with:

• 4 × NVIDIA T4 GPUs
• 16 GB VRAM per GPU
• 440 GB system RAM

Error

During model fitting, I receive the following error:

Local rendezvous is aborting with status: RESOURCE_EXHAUSTED: OOM when allocating tensor with shape [36,7000,12078] and type float on/job:localhost/replica:0/task:0/device:GPU:0by allocator GPU_0_bfc

My Interpretation

The error appears to indicate that TensorFlow/JAX is attempting to allocate a tensor that exceeds the memory available on a single T4 GPU (GPU:0).

However, I'm unsure whether:

1. This dataset size is expected to exceed the practical limits of a 16 GB T4 GPU when using Meridian.
2. There may be a configuration issue causing excessive memory usage.
3. Meridian currently utilizes only a single GPU during this stage of model fitting, despite multiple GPUs being available on the cluster.

Questions

1. Based on the model dimensions (66 weeks, 183 DMAs, 36 media channels), would you expect a T4 with 16 GB VRAM to be sufficient?
2. Does the error indicate a genuine GPU memory limitation, or would you recommend investigating other aspects of the implementation/configuration?
3. If this is primarily a memory limitation, what would you recommend as the next best option:

• Reducing the number of geographies?
• Aggregating media channels?
• Reducing posterior samples/chains?
• Moving to a larger GPU (e.g., L4, A10, A100)?

Any guidance or recommendations would be greatly appreciated.

Thank you.

[cpulavarthi]

Hello @GeethaJoseph,

Thank you for reaching out and providing clear details about your model configuration and Databricks compute environment!

Is this a hardware limitation or configuration issue?

Based on the dimensions you shared, this appears consistent with a genuine GPU memory limitation. Your model currently contains 54 independent variables (36 media channels + 10 non-media treatments + 8 controls). When combined with 183 DMAs and 66 time periods, the resulting tensor operations during MCMC sampling become incredibly memory-intensive. While a 16 GB T4 GPU is highly capable, this specific level of scale and complexity typically pushes past its practical memory limits.

Additionally, memory requirements depend heavily on your MCMC configurations (such as the number of chains and draws) and your choice of priors. If your priors are well-informed, posterior convergence may occur in fewer iterations, reducing your computational overhead. You can read more about this in our documentation on Getting MCMC Convergence.

Recommendations & Next Steps

All the solutions you proposed are completely valid. Before committing to more expensive computational resources, we recommend experimenting with your configuration.

Here is how we suggest prioritizing your next steps:

• Aggregate Lower-Contribution Variables: 54 independent variables is typically higher than what is observed in MMM studies and can substantially increase memory requirements. I would strongly suggest combining lower-contribution media channels and control variables where it reasonably aligns with your business logic.
• Establish a Smaller Baseline Test: To understand your system's limits, try drastically reducing the model complexity. For example, aggregate your independent variables to under 15, scale down to fewer than 10 geos, and reduce your chains/draws. Running a baseline test will give you insight into the tradeoffs between computational limits and the quality of your insights.
• Check Data Points per Parameter: You didn't mention the number of knots in your configuration. We highly recommend calculating the data points per parameter to ensure your configuration is optimal. You can find an example of this calculation in our Amount of Data for Geo Models documentation.
• Upgrade Hardware: If you find that aggressive aggregation compromises the business value of your study, you will likely need to scale up to a larger GPU (such as an L4, A10, or A100) with more VRAM. The previous steps can help you better account for the tradeoffs.

For additional guidance, please review the dedicated section on handling GPU OOM errors in our model debugging guide.

Let us know how your initial baseline experiments go, and please feel free to reach back out if you need further assistance.

Google Meridian Support Team