At BUZZ HPC, we shine a spotlight on breakthrough AI research that delivers real-world value you can measure in dollars and cents. In this article, we dive into MicroAdam — a game-changing optimizer that lets you fine-tune bigger models on the same GPU without touching your architecture, your data, or even your batch size.

If you’re renting H100s, H200s, or B200s from us for model training, MicroAdam can be the difference between you needing more compute resources, or cutting 50%

Optimizer State: The Hidden Cost in Your GPU Memory Budget

If you're fine-tuning a large model today, your memory bottleneck probably isn’t activations — it’s the optimizer. Popular optimizers like Adam or AdamW maintain two 32-bit tensors (first and second moments) for every parameter in your model.

That means every 7B parameter model costs an additional 50+ GB of optimizer state, even when your weights are in bf16. On an 80 GB GPU, that’s a show-stopper.

MicroAdam rewrites that equation.

What Is MicroAdam?

MicroAdam is a drop-in replacement for Adam/AdamW introduced at NeurIPS 2024. It reduces optimizer memory to under 1 byte per parameter, while maintaining full-rank accuracy across standard benchmarks.

Installation:

 pip install ista-daslab-optimizers

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Usage:

from ista_daslab_optimizers import MicroAdam
optimizer = MicroAdam(model.parameters(), lr=1e-4)

Under the hood, MicroAdam stores only a sparse window of the top 1% of gradients, augmented with a tiny 4-bit error-correcting buffer. This clever design maintains full-rank performance on a fraction of the memory footprint.

Real Benefits for BUZZ HPC Clients

Here's what that memory saving actually means when you're training on BUZZ HPC infrastructure:

For every extra 5–10 GB you squeeze out of the optimizer, you unlock more compute for what actually matters: training signal and faster convergence.

Where's the Catch?

While MicroAdam looks like a free upgrade — and largely is — there are some important caveats:

• Slight compute overhead: Sparse operations are slightly more compute-intensive than dense Adam updates. In practice, training time can decrease due to better GPU utilization.
  
  
• Hyperparameter tuning: MicroAdam introduces two new knobs — window size (m) and sparsity level (k). Defaults (m=10, k=1%) work well, but further tuning may improve results.
  
  
• Not yet standard: As of writing, MicroAdam is not built into HuggingFace Transformers or DeepSpeed. Manual integration may be required.
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• Distributed training support: DDP and FSDP require small changes to support MicroAdam’s sparse gradient updates — trivial for advanced users, but not entirely plug-and-play (yet).

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Ready to Deploy on BuzzHPC Infrastructure

BUZZ HPC makes it easy to test MicroAdam right now:

✅ VM-level GPU rentals: Use our pre-built PyTorch 2.x containers with MicroAdam pre-installed.
✅ Bare-metal H100/H200 servers: Ideal for clients pushing past 13B model sizes with single-GPU fine-tunes.
✅ Superclusters (B100/B200): Combine MicroAdam with mixed-precision and low-bit inference to squeeze out the highest model-per-dollar ratio on the planet.

Not sure where to start? Our team can help you size your cluster, choose an optimizer, and maximize model throughput per watt — all on infrastructure designed for next-gen AI.

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